In recent years online advertising has become increasingly ubiquitous and effective. Advertisements shown to visitors fund sites and apps that publish digital content, manage social networks, and operate e-mail services. Given such large variety of internet resources, determining an appropriate type of advertising for a given platform has become critical to financial success. Native advertisements, namely ads that are similar in look and feel to content, have had great success in news and social feeds. However, to date there has not been a winning formula for ads in e-mail clients. In this paper we describe a system that leverages user purchase history determined from e-mail receipts to deliver highly personalized product ads to Yahoo Mail users. We propose to use a novel neural language-based algorithm specifically tailored for delivering effective product recommendations, which was evaluated against baselines that included showing popular products and products predicted based on co-occurrence. We conducted rigorous offline testing using a large-scale product purchase data set, covering purchases of more than 29 million users from 172 e-commerce websites. Ads in the form of product recommendations were successfully tested on online traffic, where we observed a steady 9 lift in click-through rates over other ad formats in mail, as well as comparable lift in conversion rates. Following successful tests, the system was launched into production during the holiday season of 2014.
Outside of the e-mail domain, information about user's purchase history is extensively used by e-commerce websites to recommend relevant products to their users @cite_8 . Recommendation systems predict which products a user will most likely be interested in either by exploiting purchase behavior of users with similar interests (referred to as collaborative filtering @cite_8 ) or by using user's historical interaction with other products (i.e., context-based recommendation @cite_11 ). Unlike these studies, we are not limited to the data from a single website, as purchases extracted from e-mails enable us to gather information from hundreds of different e-commerce websites that can be exploited to learn better product predictions. To the best of our knowledge, this work represents the first study that offers a comprehensive empirical analysis and evaluation of product predictors using e-mail data of such scale and nature.
@cite_11 @cite_8
115528473 2159094788
In recent years online advertising has become increasingly ubiquitous and effective. Advertisements shown to visitors fund sites and apps that publish digital content, manage social networks, and operate e-mail services. Given such large variety of internet resources, determining an appropriate type of advertising for a given platform has become critical to financial success. Native advertisements, namely ads that are similar in look and feel to content, have had great success in news and social feeds. However, to date there has not been a winning formula for ads in e-mail clients. In this paper we describe a system that leverages user purchase history determined from e-mail receipts to deliver highly personalized product ads to Yahoo Mail users. We propose to use a novel neural language-based algorithm specifically tailored for delivering effective product recommendations, which was evaluated against baselines that included showing popular products and products predicted based on co-occurrence. We conducted rigorous offline testing using a large-scale product purchase data set, covering purchases of more than 29 million users from 172 e-commerce websites. Ads in the form of product recommendations were successfully tested on online traffic, where we observed a steady 9 lift in click-through rates over other ad formats in mail, as well as comparable lift in conversion rates. Following successful tests, the system was launched into production during the holiday season of 2014. Most of existing e-commerce recommender systems aim to recommend the right product to a user, based on whether the user is likely to purchase or like a product. On the other hand, the effectiveness of recommendations also depends on the time of the recommendation. Let us take a user who just purchased a laptop as an example. She may purchase a replacement battery in 2 years (assuming that the laptop's original battery often fails to work around that time) and purchase a new laptop in another 2 years. In this case, it is not a good idea to recommend a new laptop or a replacement battery right after the user purchased the new laptop. It could hurt the user's satisfaction of the recommender system if she receives a potentially right product recommendation at the wrong time. We argue that a system should not only recommend the most relevant item, but also recommend at the right time. This paper studies the new problem: how to recommend the right product at the right time? We adapt the proportional hazards modeling approach in survival analysis to the recommendation research field and propose a new opportunity model to explicitly incorporate time in an e-commerce recommender system. The new model estimates the joint probability of a user making a follow-up purchase of a particular product at a particular time. This joint purchase probability can be leveraged by recommender systems in various scenarios, including the zero-query pull-based recommendation scenario (e.g. recommendation on an e-commerce web site) and a proactive push-based promotion scenario (e.g. email or text message based marketing). We evaluate the opportunity modeling approach with multiple metrics. Experimental results on a data collected by a real-world e-commerce website(shop.com) show that it can predict a user's follow-up purchase behavior at a particular time with descent accuracy. In addition, the opportunity model significantly improves the conversion rate in pull-based systems and the user satisfaction utility in push-based systems. In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users' social media profiles and their e-commerce behaviors. This paper presents a system for predicting a user's purchase behaviors on e-commerce websites from the user's social media profile. We specifically aim at understanding if the user's profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). The paper provides an extensive analysis on how users' Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction. The development of Web 2.0 technology has led to huge economic benefits and challenges for both e-commerce websites and online shoppers. One core technology to increase sales and consumers' satisfaction is the use of recommender systems. Existing product recommender systems consider the order of items purchased by users to obtain a list of recommended items. However, they do not consider the time interval between the products purchased. For example, there is often an interval of 2-3 months between the purchase of printer ink cartridges or refills. Thus, recommending appropriate ink cartridges one week before the user needs to replace the depleted ink cartridges would increase the likelihood of a purchase decision. In this paper, we propose to utilize the purchase interval information to improve the performance of the recommender systems for e-commerce. We design an efficient algorithm to compute the purchase intervals between product pairs from users' purchase history and integrate this information into the marginal utility model. We evaluate our approach on a real world ecommerce dataset. Experimental results demonstrate that our approach significantly improves the conversion rate and temporal diversity compared to state-of-the-art algorithms. Each user session in an e-commerce system can be modeled as a sequence of web pages, indicating how the user interacts with the system and makes his her purchase. A typical recommendation approach, e.g., Collaborative Filtering, generates its results at the beginning of each session, listing the most likely purchased items. However, such approach fails to exploit current viewing history of the user and hence, is unable to provide a real-time customized recommendation service. In this paper, we build a deep recurrent neural network to address the problem. The network tracks how users browse the website using multiple hidden layers. Each hidden layer models how the combinations of webpages are accessed and in what order. To reduce the processing cost, the network only records a finite number of states, while the old states collapse into a single history state. Our model refreshes the recommendation result each time when user opens a new web page. As user's session continues, the recommendation result is gradually refined. Furthermore, we integrate the recurrent neural network with a Feedfoward network which represents the user-item correlations to increase the prediction accuracy. Our approach has been applied to Kaola (http: www.kaola.com), an e-commerce website powered by the NetEase technologies. It shows a significant improvement over previous recommendation service. Nowadays customers would rather buy their needs online than visiting a retail store because of many reasons such as saving time. Therefore, in order to increase efficiency of online shopping websites, many companies have invested in researches toward prediction of users purchases and recommendation systems that may help and motivate a user to buy products that he may be interested in. However, most efforts in this area has been around classification and predictions based on users interests in specific types of products. In this paper, we have studied efficiency of numerous algorithms toward building a classification model to predict the probability of a complete purchase by users only based on their behavior models in the system and regardless of their interest. Therefore, we experimented accuracy of different algorithms and proposed a novel classification model that is able to predict whether a user will be interested in buying a certain set of products that are placed in the online shopping cart or not. The frequently changing user preferences and or item profiles have put essential importance on the dynamic modeling of users and items in personalized recommender systems. However, due to the insufficiency of per user item records when splitting the already sparse data across time dimension, previous methods have to restrict the drifting purchasing patterns to pre-assumed distributions, and were hardly able to model them rather directly with, for example, time series analysis. Integrating content information helps to alleviate the problem in practical systems, but the domain-dependent content knowledge is expensive to obtain due to the large amount of manual efforts. In this paper, we make use of the large volume of textual reviews for the automatic extraction of domain knowledge, namely, the explicit features aspects in a specific product domain. We thus degrade the product-level modeling of user preferences, which suffers from the lack of data, to the feature-level modeling, which not only grants us the ability to predict user preferences through direct time series analysis, but also allows us to know the essence under the surface of product-level changes in purchasing patterns. Besides, the expanded feature space also helps to make cold-start recommendations for users with few purchasing records. Technically, we develop the Fourier-assisted Auto-Regressive Integrated Moving Average (FARIMA) process to tackle with the year-long seasonal period of purchasing data to achieve daily-aware preference predictions, and we leverage the conditional opportunity models for daily-aware personalized recommendation. Extensive experimental results on real-world cosmetic purchasing data from a major e-commerce website (JD.com) in China verified both the effectiveness and efficiency of our approach. Predicting customer purchase behaviour is an interesting and challenging task. In e-commerce context, to tackle the challenge will confront a lot of new problems different from those in traditional business. This study investigates three factors that affect purchasing decision-making of customers in online shopping: the needs of customers, the popularity of products and the preference of the customers. Furthermore, exploiting purchase data and ratings of products in the e-commerce website, we propose methods to quantify the strength of these factors: (1) using associations between products to predict the needs of customers; (2) combining collaborative filtering and a hierarchical Bayesian discrete choice model to learn preference of customers; (3) building a support vector regression based model, called Heat model, to calculate the popularity of products; (4) developing a crowdsourcing approach based experimental platform to generate train set for learning Heat model. Combining these factors, a model, called COREL, is proposed to make purchase behaviour prediction for customers. Submitted a purchased product of a customer, the model can return top n the most possible purchased products of the customer in future. Experiments show that these factors play key roles in predictive model and COREL can greatly outperform the baseline methods. In this paper, we design a recommender system for the post-purchase stage, i.e., after a user purchases a product. Our method combines both behavioral and content aspects of recommendations. We first find the most related categories for the active product in the post-purchase stage. Among these related categories, products with high behavioral relevance and content relevance are recommended to the user. In addition, our algorithm considers the temporal factor, i.e., the purchase time of the active product and the recommendation time. We apply our algorithm on a random sample of the purchase data from eBay. Comparing to the baseline item-based collaborative filtering approach, our hybrid recommender system achieves significant coverage and purchase rate gain for different time windows. Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of online user reviews, the information available for training a recommender system is no longer limited to just numerical star ratings or user item features. By extracting explicit user opinions about various aspects of a product from the reviews, it is possible to learn more details about what aspects a user cares, which further sheds light on the possibility to make explainable recommendations. In this work, we propose the Explicit Factor Model (EFM) to generate explainable recommendations, meanwhile keep a high prediction accuracy. We first extract explicit product features (i.e. aspects) and user opinions by phrase-level sentiment analysis on user reviews, then generate both recommendations and disrecommendations according to the specific product features to the user's interests and the hidden features learned. Besides, intuitional feature-level explanations about why an item is or is not recommended are generated from the model. Offline experimental results on several real-world datasets demonstrate the advantages of our framework over competitive baseline algorithms on both rating prediction and top-K recommendation tasks. Online experiments show that the detailed explanations make the recommendations and disrecommendations more influential on user's purchasing behavior. Abstract : We investigate the use of dimensionality reduction to improve performance for a new class of data analysis software called "recommender systems" Recommender systems apply knowledge discovery techniques to the problem of making product recommendations during a live customer interaction. These systems are achieving widespread success in E-commerce nowadays, especially with the advent of the Internet. The tremendous growth of customers and products poses three key challenges for recommender systems in the E-commerce domain. These are: producing high quality recommendations, performing many recommendations per second for millions of customers and products, and achieving high coverage in the face of data sparsity. One successful recommender system technology is collaborative filtering, which works by matching customer preferences to other customers in making recommendations. Collaborative filtering has been shown to produce high quality recommendations, but the performance degrades with the number of customers and products. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very largescale problems. This paper presents two different experiments where we have explored one technology called Singular Value Decomposition (SVD) to reduce the dimensionality of recommender system databases. Each experiment compares the quality of a recommender system using SVD with the quality of a recommender system using collaborative filtering. The first experiment compares the effectiveness of the two recommender systems at predicting consumer preferences based on a database of explicit ratings of products. The second experiment compares the effectiveness of the two recommender systems at producing Top-N lists based on a real-life customer purchase database from an E-Commerce site. Our experience suggests that SVD has the potential to meet many of the challenges of recommender systems, under certain conditions. Previous studies on informational cascades have stressed the importance of informational social influences in decision-making. When people use the product evaluations of others to indicate product quality on the Internet, online herd behavior occurs. This work presents four studies examining herd behavior of online book purchasing. The first two studies addressed how two cues frequently found on the Internet, i.e., star ratings and sales volume, influence consumer online product choices. The last two studies investigated the relative effectiveness of different recommendation sources. The experimental results demonstrated that subjects use the product evaluations and choices of others as cues in making purchasing book decisions on the Internet bookstore. Additionally, recommendations of other consumers exerted a greater influence on subject choices than recommendations of an expert. Finally, recommendations from recommender system influenced online consumer choices more than those from website owners. The results and implications of this research are discussed. E-commerce has revolutionised how we browse and purchase products and services globally. However, with revolution comes disruption as retailers and users struggle to keep up with the pace of change. Retailers are increasingly using a varied number of machine learning techniques in areas such as information retrieval, user interface design, product catalogue curation and sentiment analysis, all of which must operate at scale and in near real-time. Understanding user purchase intent is important for a number of reasons. Buyers typically represent <5 of all e-commerce users, but contribute virtually all of the retailer profit. Merchants can cost-effectively target measures such as discounting, special offers or enhanced advertising at a buyer cohort - something that would be cost prohibitive if applied to all users. We used supervised classic machine learning and deep learning models to infer user purchase intent from their clickstreams. Our contribution is three-fold: first we conducted a detailed analysis of explicit features showing that four broad feature classes enable a classic model to infer user intent. Second, we constructed a deep learning model which recovers over 98 of the predictive power of a state-of-the-art approach. Last, we show that a standard word language deep model is not optimal for e-commerce clickstream analysis and propose a combined sampling and hidden state management strategy to improve the performance of deep models in the e-commerce domain. We also propose future work in order to build on the results obtained. In personalized recommendation, candidate generation plays an infrastructural role by retrieving candidates out of billions of items. During this process, substitutes and complements constitute two main classes of retrieved candidates: substitutable products are interchangeable, whereas complementary products might be purchased together by users. Discriminating substitutable and complementary products is playing an increasingly important role in e-commerce portals by affecting the performance of candidate generation, e.g., when a user has browsed a t-shirt, it is reasonable to retrieve similar t-shirts, i.e., substitutes; whereas if the user has already purchased one, it would be better to retrieve trousers, hats or shoes, as complements of t-shirts. In this paper, we propose a path-constrained framework (PMSC) for discriminating substitutes and complements. Specifically, for each product, we first learn its embedding representations in a general semantic space. Thereafter, we project the embedding vectors into two separate spaces via a novel mapping function. In the end, we incorporate each embedding with path-constraints to further boost the discriminative ability of the model. Extensive experiments conducted on two e-commerce datasets show the effectiveness of our proposed method. In a modern recommender system, it is important to understand how products relate to each other. For example, while a user is looking for mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Here we develop a method to infer networks of substitutable and complementary products. We formulate this as a supervised link prediction task, where we learn the semantics of substitutes and complements from data associated with products. The primary source of data we use is the text of product reviews, though our method also makes use of features such as ratings, specifications, prices, and brands. Methodologically, we build topic models that are trained to automatically discover topics from text that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews. Many commercial websites use recommender systems to help customers locate products and content. Modern recommenders are based on collaborative filtering: they use patterns learned from users' behavior to make recommendations, usually in the form of related-items lists. The scale and complexity of these systems, along with the fact that their outputs reveal only relationships between items (as opposed to information about users), may suggest that they pose no meaningful privacy risk. In this paper, we develop algorithms which take a moderate amount of auxiliary information about a customer and infer this customer's transactions from temporal changes in the public outputs of a recommender system. Our inference attacks are passive and can be carried out by any Internet user. We evaluate their feasibility using public data from popular websites Hunch, Last. fm, Library Thing, and Amazon. Clickstream data provide information about the sequence of pages or the path viewed by users as they navigate a website. We show how path information can be categorized and modeled using a dynamic multinomial probit model of Web browsing. We estimate this model using data from a major online bookseller. Our results show that the memory component of the model is crucial in accurately predicting a path. In comparison, traditional multinomial probit and first-order Markov models predict paths poorly. These results suggest that paths may reflect a user's goals, which could be helpful in predicting future movements at a website. One potential application of our model is to predict purchase conversion. We find that after only six viewings purchasers can be predicted with more than 40 accuracy, which is much better than the benchmark 7 purchase conversion prediction rate made without path information. This technique could be used to personalize Web designs and product offerings based upon a user's path. Recommender systems (RS) have shown to be valuable tools on e-commerce sites which help the customers identify the most relevant items within large product catalogs. In systems that rely on collaborative filtering, the generation of the product recommendations is based on ratings provided by the user community. While in many domains users are only allowed to attach an overall rating to the items, increasingly more online platforms allow their customers to evaluate the available items along different dimensions. Previous work has shown that these criteria ratings contain valuable information that can be exploited in the recommendation process. In this work we present new methods to leverage information derived from multi-dimensional ratings to improve the predictive accuracy of such multi-criteria recommender systems. In particular, we propose to use Support Vector regression to determine the relative importance of the individual criteria ratings and suggest to combine user- and item-based regression models in a weighted approach. Beside the automatic adjustment and optimization of the combination weights, we also explore different feature selection strategies to further improve the quality of the recommendations. An experimental analysis on two real-world rating datasets reveals that our method outperforms both recent single-rating algorithms based on matrix factorization as well as previous methods based on multi-criteria ratings in terms of the predictive accuracy. We therefore see the usage of multi-criteria customer ratings as a promising opportunity for e-commerce sites to improve the quality and precision of their online recommendation services. We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective. Recommender systems support users in identifying products and services in e-commerce and other information-rich environments. Recommendation problems have a long history as a successful AI application area, with substantial interest beginning in the mid-1990s, and increasing with the subsequent rise of e-commerce. Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services. In this paper, we first introduce a taxonomy of recommendation knowledge sources and algorithmic approaches. We then go on to discuss the most prevalent techniques of constraint-based recommendation and outline open research issues.