Mobile Context-Aware Recommender Systems can be naturally modelled as an exploration exploitation trade-off (exr exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration). This problem has been addressed by the reinforcement learning community but they do not consider the risk level of the current user's situation, where it may be dangerous to recommend items the user may not desire in her current situation if the risk level is high. We introduce in this paper an algorithm named R-UCB that considers the risk level of the user's situation to adaptively balance between exr and exp. The detailed analysis of the experimental results reveals several important discoveries in the exr exp behaviour.
In @cite_16 the authors propose a mobile recommender system for people in leisure time. The system predicts the current activity (eating,reading or shopping) from the context (time, location) and the behaviour of the user. The predicted activity combined with preference models of the user, are used to filter and recommend relevant content. To provide relevant advertisements to mobile users, authors in @cite_15 build a profile of each region visited by the user. Statistical techniques are then used to extract information from the visited regions, like "the frequency of visits", "duration and time of typical visits", and the user's profile is built on the basis of questionnaires.
@cite_15 @cite_16
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Insights into the behavior and preference of mobile device users from their web browsing application activities are critical components of any successful dynamic content recommendation system, mobile advertisement platform, or web personalization initiative. In this paper we use an unsupervised topic model to understand the interests of the cellular users based upon their browsing profile. We posit that the length of time a user remains on a given website is positively correlated with the user’s interest in the website’s content. We propose an extended model to integrate this duration information efficiently by oversampling the URLs. This paper presents a context-aware mobile recommender system, codenamed Magitti. Magitti is unique in that it infers user activity from context and patterns of user behavior and, without its user having to issue a query, automatically generates recommendations for content matching. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea, shaped the details of its design and provided data describing typical behavior patterns. The paper describes the fieldwork, user interface, system components and functionality, and an evaluation of the Magitti prototype. 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. Recommendation Systems have become an important research area in mobile computing. Although various recommendation systems have been developed to help users to deal with information overload, few systems focus on proactive information recommendation. This paper presents a news recommender system that proactively pushes just-in-time personalized news articles to mobile users based on user’s contextual information as well as news content. User’s information needs are estimated based on Bayesian network technique. An Analytic Hierarchy Process (AHP) Model, which supports both Content-based filtering and Collaborative filtering, is developed to rate the relevance of news articles. The weight of contexts (criteria) is automatically adjusted via individual-based and or group-based (group decision making) assignment. The experiments show that the system can push relevant news to mobile users. A common scenario considered in recommender systems is to predict a user's preferences on unseen items based on his her preferences on observed items. A major limitation of this scenario is that a user might be interested in different things each time when using the system, but there is no way to allow the user to actively alter or adjust the recommended results. To address this issue, we propose the idea of "query-based recommendation" that allows a user to specify his her search intention while exploring new items, thereby incorporating the concept of information retrieval into recommendation systems. Moreover, the idea is more desirable when the user intention can be expressed in different ways. Take music recommendation as an example: the proposed system allows a user to explore new song tracks by specifying either a track, an album, or an artist. To enable such heterogeneous queries in a recommender system, we present a novel technique called "Heterogeneous Preference Embedding" to encode user preference and query intention into low-dimensional vector spaces. Then, with simple search methods or similarity calculations, we can use the encoded representation of queries to generate recommendations. This method is fairly flexible and it is easy to add other types of information when available. Evaluations on three music listening datasets confirm the effectiveness of the proposed method over the state-of-the-art matrix factorization and network embedding methods. The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple users to recommend items of interest to other users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed and evaluated many approaches for generating recommendations. We describe and evaluate a new method called personality diagnosis (PD). Given a user's preferences for some items, we compute the probability that he or she is of the same "personality type" as other users, and, in turn, the probability that he or she will like new items. PD retains some of the advantages of traditional similarity-weighting techniques in that all data is brought to bear on each prediction and new data can be added easily and incrementally. Additionally, PD has a meaningful probabilistic interpretation, which may be leveraged to justify, explain, and augment results. We report empirical results on the EachMovie database of movie ratings, and on user profile data collected from the CiteSeer digital library of Computer Science research papers. The probabilistic framework naturally supports a variety of descriptive measurements--in particular, we consider the applicability of a value of information (VOI) computation. This paper describes a novel approach for gathering profile information about mobile phone users. The focus is on information that can be used to enhance targeting of advertisements. (The ads might be delivered into the mobile phones, or to other devices such as the user's IPTV.) Unlike previous approaches, we use a two-tiered approach for learning end-user habits and preferences. In this approach the first tier involves statistical learning from network observable data (in the current paper, primarily logs of cell towers visited), and the second tier involves explicit queries to the user (in the current paper, to ask, e.g., what kinds of activities the user does in a given region that he frequents). The user might be willing to answer occasional queries of this sort through offers of service discounts, or to be able to receive more relevant ads. The paper focuses on two key aspects of our approach, which correspond to how the two tiers are instantiated in the current version of the prototype system that we have developed at Bell Labs. The first concerns the statistical techniques used to determine information about regions visited, along with the frequency of visits, typical durations, and typical visit times. These techniques were developed based on a training set consisting of logs of 6 users with mobile devices over a period of several months. The techniques address issues that arise when a given small region is serviced by multiple cell towers (in which case oscillations between cell towers can be confused with movement between regions). The second key aspect concerns optimizing the order in which queries are presented to users, in a context where different query answers have different value for the advertising process. (The values of answers might be influenced by the mix of advertising campaigns from which ads are to be matched against users.) Optimization is NP-complete in a relatively general context. We develop a polynomial time algorithm which yields optimal sequences for the case where the family of queries to be asked satisfies a tree-based property. This is extended to create a heuristic polynomial time algorithm for the general case. Recommender systems solve an information filtering task. They suggest data objects that seem likely to be relevant to the user based upon previous choices that this user has made. A geographic recommender system recommends items from a library of georeferenced objects such as photographs of touristic sites. A widely-used approach to recommending consists in suggesting the most popular items within the user community. However, these approaches are not able to handle individual differences between users. We ask how to identify less popular geographic objects that are nevertheless of interest to a specific user. Our approach is based on user-based collaborative filtering in conjunction with an prototypical model of geographic places (heatmaps). We discuss four different measures of similarity between users that take into account the spatial semantic derived from the spatial behavior of a user community. We illustrate the method with a real-world use case: recommendations of georeferenced photographs from the public website Panoramio. The evaluation shows that our approach achieves a better recall and precision for the first ten items than recommendations based on the most popular geographic items. This paper presents a successful attempt at evolving web intelligence in the tourism scenario, namely throughout two main areas: User Modeling and Recommender Systems. The first subject deals with the correct modeling of tourists’ profiles using a wide variety of techniques, such as stereotypes, keywords and psychological models. These techniques, besides presenting user interests with great coherence and completeness, allow for the reduction of several current problems such as the cold start issue, gray sheep individuals and overspecialization. The recommender system, by making use of all user models’ building blocks, brings an interesting, innovative and hybrid nature to the area, with benefits such as behavioral filtering, multi-technique resourcefulness and on-the-fly suggestions. The architecture was already tested in the scope of a prototype regarding the city of Porto, in Portugal. The prevalence of smartphones and mobile social networks allow the users to share their location-based life experience much easier. The large amount of data generated in related location-based social networks provides informative cues on user's behaviors and preferences to support personalized location-based services, like point-of-interest (POI) recommendation. Yet achieving accurate personalized POI recommendation is challenging as the data available for each user is highly sparse. In addition, the computational complexity is high due to the large number of users. In this paper, a novel methodology for personalized successive POI recommendation is proposed. First, the preferred successive category of location is predicted using a third-rank tensor computed based on the partially observed transitions between the categories of user's successive locations where the missing transitions are uncovered by inferring the group preference. The group is achieved according to users' demographics and frequently visited locations. Then, a bipartite graph is constructed based on the recommended categories for each user. To obtain the personalized ranking of locations, a distance weighted HITS algorithm is proposed so that the location authority score is updated iteratively according to the visiting frequency of the group and some distance constraints. The proposed two-step approach with the category prediction incorporated aims to boost the location prediction performance via the smoothing and at the same time reduce the complexity. Experimental results obtained based on the real-world location-based social network data show that the proposed approach outperforms the existing state-of-the-art methods by a large margin. Mobile news recommender systems help users retrieve news that is relevant in their particular context and can be presented in ways that require minimal user interaction. In spite of the availability of contextual information about mobile users, though, current mobile news applications employ rather simple strategies for news recommendation. Our multi-perspective approach unifies temporal, locational, and preferential information to provide a more fine-grained recommendation strategy. This demo paper presents the implementation of our solution to efficiently recommend specific news articles from a large corpus of newly-published press releases in a way that closely matches a reader's reading preferences. In this work, we discover the daily location-driven routines that are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16-month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include “going to work late”, “going home early”, “working nonstop” and “having no reception (phone off)” at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as “going to work early” or “turning off the mobile (or having no reception) in the evenings”. We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events. Mobile computing adds a mostly unexplored dimension to data mining: user's position is a relevant piece of information, and recommendation systems, selecting and ranking links of interest to the user, have the opportunity to take location into account. In this paper a mobility-aware recommendation system that considers the location of the user to filter recommended links is proposed. To avoid the potential problems and costs of insertion by hand, a new middleware layer, the location broker, maintains a historic database of locations and corresponding links used in the past and develops models relating resources to their spatial usage pattern. These models are used to calculate a preference metric when the current user is asking for resources of interest. Mobility scenarios are described and analyzed in terms of possible user requirements. The features of the PILGRIM mobile recommendation system are outlined together with a preliminary experimental evaluation of different metrics. In order to generate relevant recommendations, a context-aware recommender system (CARS) not only makes use of user preferences, but also exploits information about the specific contextual situation in which the recommended item will be consumed. For instance, when recommending a holiday destination, a CARS could take into account whether the trip will happen in summer or winter. It is unclear, however, which contextual factors are important and to which degree they influence user ratings. A large amount of data and complex context-aware predictive models must be exploited to understand these relationships. In this paper, we take a new approach for assessing and modeling the relationship between contextual factors and item ratings. Rather than using the traditional approach to data collection, where recommendations are rated with respect to real situations as participants go about their lives as normal, we simulate contextual situations to more easily capture data regarding how the context influences user ratings. To this end, we have designed a methodology whereby users are asked to judge whether a contextual factor (e.g., season) influences the rating given a certain contextual condition (e.g., season is summer). Based on the analyses of these data, we built a context-aware mobile recommender system that utilizes the contextual factors shown to be important. In a subsequent user evaluation, this system was preferred to a similar variant that did not exploit contextual information. Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user's content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration exploitation and can adaptively balance the two aspects by deciding which user's situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms. Recommender systems (RS) play an important role in many areas including targeted advertising, personalized marketing and information retrieval. In recent years, the importance of contextual information has attracted many researchers to focus on designing systems that produce personalized recommendations in accordance with available contextual information of users. Comparing with traditional RS which mainly utilize users’ rating history, the context# aware recommender systems (CARS) can result in better performance in various applications. In this paper, we present a context#aware recommender system that extracts contextual information from a textual description of user current situation and use it in combination with user ratings history to compute a utility function over the set of items. The item utility shows how much it is preferable regarding user current context. In our system, the context inference is modeled as a supervised topic#modeling problem in which the set of categories for a contextual attribute constitutes the topic set. As an example application, we used our method to mine hidden contextual data from customers' reviews for hotels and use it to produce context#aware recommendations. Our evaluations suggest that our system can help produce better recommendations in comparison to a standard kNN recommender system. Preparing recommendations for unknown users or such that correctly respond to the short-term needs of a particular user is one of the fundamental problems for e-commerce. Most of the common Recommender Systems assume that user identification must be explicit. In this paper a Session-Aware Recommender System approach is presented where no straightforward user information is required. The recommendation process is based only on user activity within a single session, defined as a sequence of events. This information is incorporated in the recommendation process by explicit context modeling with factorization methods and a novel approach with Recurrent Neural Network (RNN). Compared to the session modeling approach, RNN directly models the dependency of user observed sequential behavior throughout its recurrent structure. The evaluation discusses the results based on sessions from real-life system with ephemeral items (identified only by the set of their attributes) for the task of top-n best recommendations. Internet-based recommender systems have traditionally employed collaborative filtering techniques to deliver relevant "digital" results to users. In the mobile Internet however, recommendations typically involve "physical" entities (e.g., restaurants), requiring additional user effort for fulfillment. Thus, in addition to the inherent requirements of high scalability and low latency, we must also take into account a "convenience" metric in making recommendations. In this paper, we propose an enhanced collaborative filtering solution that uses location as a key criterion for generating recommendations. We frame the discussion in the context of our "restaurant recommender" system, and describe preliminary results that indicate the utility of such an approach. We conclude with a look at open issues in this space, and motivate a future discussion on the business impact and implications of mining the data in such systems. The popularity of location-based social networks provide us with a new platform to understand users' preferences based on their location histories. In this paper, we present a location-based and preference-aware recommender system that offers a particular user a set of venues (such as restaurants) within a geospatial range with the consideration of both: 1) User preferences, which are automatically learned from her location history and 2) Social opinions, which are mined from the location histories of the local experts. This recommender system can facilitate people's travel not only near their living areas but also to a city that is new to them. As a user can only visit a limited number of locations, the user-locations matrix is very sparse, leading to a big challenge to traditional collaborative filtering-based location recommender systems. The problem becomes even more challenging when people travel to a new city. To this end, we propose a novel location recommender system, which consists of two main parts: offline modeling and online recommendation. The offline modeling part models each individual's personal preferences with a weighted category hierarchy (WCH) and infers the expertise of each user in a city with respect to different category of locations according to their location histories using an iterative learning model. The online recommendation part selects candidate local experts in a geospatial range that matches the user's preferences using a preference-aware candidate selection algorithm and then infers a score of the candidate locations based on the opinions of the selected local experts. Finally, the top-k ranked locations are returned as the recommendations for the user. We evaluated our system with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than baselines, while having a good efficiency of providing location recommendations. With the increasing popularity of location-based services, we have accumulated a lot of location data on the Web. In this paper, we are interested in answering two popular location-related queries in our daily life: (1) if we want to do something such as sightseeing or dining in a large city like Beijing, where should we go? (2) If we want to visit a place such as the Bird@?s Nest in Beijing Olympic park, what can we do there? We develop a mobile recommendation system to answer these queries. In our system, we first model the users@? location and activity histories as a user-location-activity rating tensor. Because each user has limited data, the resulting rating tensor is essentially very sparse. This makes our recommendation task difficult. In order to address this data sparsity problem, we propose three algorithms based on collaborative filtering. The first algorithm merges all the users@? data together, and uses a collective matrix factorization model to provide general recommendation (, 2010 [3]). The second algorithm treats each user differently and uses a collective tensor and matrix factorization model to provide personalized recommendation (, 2010 [4]). The third algorithm is a new algorithm which further improves our previous two algorithms by using a ranking-based collective tensor and matrix factorization model. Instead of trying to predict the missing entry values as accurately as possible, it focuses on directly optimizing the ranking loss w.r.t. user preferences on the locations and activities. Therefore, it is more consistent with our ultimate goal of ranking locations activities for recommendations. For these three algorithms, we also exploit some additional information, such as user-user similarities, location features, activity-activity correlations and user-location preferences, to help the CF tasks. We extensively evaluate our algorithms using a real-world GPS dataset collected by 119 users over 2.5 years. We show that all our three algorithms can consistently outperform the competing baselines, and our newly proposed third algorithm can also outperform our other two previous algorithms.