Learning preferences of new users in recommender systems

Abstract

Recommender systems are an effective tool to help find items of interest from an overwhelming number of available items. Collaborative Filtering (CF), the best known technology for recommender systems, is based on the idea that a set of like-minded users can help each other find useful information. A new user poses a challenge to CF recommenders, since the system has no knowledge about the preferences of the new user, and therefore cannot provide personalized recommendations. A new user preference elicitation strategy needs to ensure that the user does not a) abandon a lengthy signup process, and b) lose interest in returning to the site due to the low quality of initial recommendations. We extend the work of [23] in this paper by incrementally developing a set of information theoretic strategies for the new user problem. We propose an offline simulation framework, and evaluate the strategies through extensive offline simulations and an online experiment with real users of a live recommender system.

Personality-Based Active Learning for Collaborative Filtering Recommender Systems

“…In [18] the authors extend their former work using a rating elicitation approach based on decision trees. The proposed technique is called IGCN , and builds a tree where each node is labelled by a particular item to be asked to the user to rate.…”

Alleviating the new user problem in collaborative filtering by exploiting personality information

“…In two separate works [57,58], Rashid et al proposed eight techniques that CF systems can use to acquire user preferences in the sign-up stage: entropy, where items with the largest rating entropy are preferred; random; popularity; log(popularity) * entropy, where items that are both popular and have diverse ratings are preferred; and finally item-item personalized, where the items are proposed randomly until one rating is acquired, and then a recommender system is used to predict the ratings for items that the user is likely to have seen; IGCN , which builds a tree where each node is labeled by a particular item to be asked to the user to rate; and Entropy0, which extends the entropy method by considering the missing value as a possible rating (category 0). They conducted offline and online simulations, and concluded that IGCN and Entropy0 perform the best in terms of accuracy.…”

“…However, regardless of the specific variant that is used, CF methods have a common limitation: the so called new user cold-start problem, which occurs when a system cannot generate personalized and relevant recommendations for a user who has just registered into the system. Although many solutions have been proposed [23,24,33,56,58,72,47,69], this problem is still challenging, and there is not a unique solution for it that can be applied to any domain or situation. Indeed, as we shall show later, different approaches better suit specific situations, e.g., when the new user has entered either zero or only a few ratings.…”

“…In general, in AL it has been shown that asking a user to provide ratings for a set of selected items can improve the accuracy of CF [65,23,21,22,57,58]. Traditional active learning methods need some pre-existent ratings in order to select even more ratings to collect from the user.…”

User-Centric vs. System-Centric Evaluation of Recommender Systems

“…This choice is motivated by three specific reasons: (i) we want to support users who have no rating history or who are not interested in logging into the system; (ii) we are interested in exploring a smooth integration of personalized recommendations in existing online booking systems; to enable explicit elicitation would require the introduction of an intrusive add-on; (iii) according to a large number of works, the lower effort of implicit elicitation (as compared to explicit elicitation) is related to higher perceived effectiveness of recommendations [9,10,14,23]. The implicit elicitation mechanism adopted in PoliVenus is the following: whenever a user interacts with an object on the interface, the system assigns a score to the hotel related to that object (e.g.…”