Recommender systems associated with social networks often use social explanations (e.g. "X, Y and 2 friends like this") to support the recommendations. We present a study of the effects of these social explanations in a music recommendation context. We start with an experiment with 237 users, in which we show explanations with varying levels of social information and analyze their effect on users' decisions. We distinguish between two key decisions: the likelihood of checking out the recommended artist, and the actual rating of the artist based on listening to several songs. We find that while the explanations do have some influence on the likelihood, there is little correlation between the likelihood and actual (listening) rating for the same artist. Based on these insights, we present a generative probabilistic model that explains the interplay between explanations and background information on music preferences, and how that leads to a final likelihood rating for an artist. Acknowledging the impact of explanations, we discuss a general recommendation framework that models external informational elements in the recommendation interface, in addition to inherent preferences of users.
However, being persuasive has drawbacks. Another study found that although explanations might persuade a user to try an item, they were not good for accurately estimating the quality of an item @cite_24 . The authors further argue the goal of a recommender should not be to promote a recommendation (which they call ), but rather enable a user to make a more accurate judgment on the true quality of the item for that person (which they call ).
@cite_24
2130369780 2018371603 2148943147 2015290529
Recommender systems have become a popular technique for helping users select desirable books, movies, music and other items. Most research in the area has focused on developing and evaluating algorithms for efficiently producing accurate recommendations. However, the ability to effectively explain its recommendations to users is another important aspect of a recommender system. The only previous investigation of methods for explaining recommendations showed that certain styles of explanations were effective at convincing users to adopt recommendations (i.e. promotion) but failed to show that explanations actually helped users make more accurate decisions (i.e. satisfaction). We present two new methods for explaining recommendations of contentbased and or collaborative systems and experimentally show that they actually improve user’s estimation of item quality. Recommender Systems (RSs) help users search large amounts of digital contents and services by allowing them to identify the items that are likely to be more attractive or useful. RSs play an important persuasion role, as they can potentially augment the users’ trust towards in an application and orient their decisions or actions towards specific directions. This article explores the persuasiveness of RSs, presenting two vast empirical studies that address a number of research questions. First, we investigate if a design property of RSs, defined by the statistically measured quality of algorithms, is a reliable predictor of their potential for persuasion. This factor is measured in terms of perceived quality, defined by the overall satisfaction, as well as by how users judge the accuracy and novelty of recommendations. For our purposes, we designed an empirical study involving 210 subjects and implemented seven full-sized versions of a commercial RS, each one using the same interface and dataset (a subset of Netflix), but each with a different recommender algorithm. In each experimental configuration we computed the statistical quality (recall and F-measures) and collected data regarding the quality perceived by 30 users. The results show us that algorithmic attributes are less crucial than we might expect in determining the user’s perception of an RS’s quality, and suggest that the user’s judgment and attitude towards a recommender are likely to be more affected by factors related to the user experience. Second, we explore the persuasiveness of RSs in the context of large interactive TV services. We report a study aimed at assessing whether measurable persuasion effects (e.g., changes of shopping behavior) can be achieved through the introduction of a recommender. Our data, collected for more than one year, allow us to conclude that, (1) the adoption of an RS can affect both the lift factor and the conversion rate, determining an increased volume of sales and influencing the user’s decision to actually buy one of the recommended products, (2) the introduction of an RS tends to diversify purchases and orient users towards less obvious choices (the long tail), and (3) the perceived novelty of recommendations is likely to be more influential than their perceived accuracy. Overall, the results of these studies improve our understanding of the persuasion phenomena induced by RSs, and have implications that can be of interest to academic scholars, designers, and adopters of this class of systems. A common approach to designing Recommender Systems (RS) consists of asking users to explicitly rate items in order to collect feedback about their preferences. However, users have been shown to be inconsistent and to introduce a non-negligible amount of natural noise in their ratings that affects the accuracy of the predictions. In this paper, we present a novel approach to improve RS accuracy by reducing the natural noise in the input data via a preprocessing step. In order to quantitatively understand the impact of natural noise, we first analyze the response of common recommendation algorithms to this noise. Next, we propose a novel algorithm to denoise existing datasets by means of re-rating: i.e. by asking users to rate previously rated items again. This denoising step yields very significant accuracy improvements. However, re-rating all items in the original dataset is unpractical. Therefore, we study the accuracy gains obtained when re-rating only some of the ratings.In particular, we propose two partial denoising strategies: data and user-dependent denoising. Finally, we compare the value of adding a rating of an unseen item vs. re-rating an item. We conclude with a proposal for RS to improve the quality of their user data and hence their accuracy: asking users to re-rate items might, in some circumstances, be more beneficial than asking users to rate unseen items. 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.