Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings

[Abstract] Nowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of the quality of the suggestions. However, novel and diverse suggestions also contribute to user satisfaction. Unfortunately, it is common to harm those two aspects when optimizing recommendation accuracy. In this paper, we present EER, a linear model for the top-N recommendation task, which takes advantage of user and item embeddings for improving novelty and diversity without harming accuracy.This work was supported by project RTI2018-093336-B-C22 (MCIU & ERDF), project GPC ED431B 2019/03 (Xunta de Galicia & ERDF) and accreditation ED431G 2019/01 (Xunta de Galicia & ERDF). The first author also acknowledges the support of grant FPU17/03210 (MCIU)Xunta de Galicia; ED431B 2019/03Xunta de Galicia; ED431G 2019/0

Top-N Recommender System via Matrix Completion

Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.Comment: AAAI 201

What’s going on in my city? Recommender systems and electronic participatory budgeting

In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities – Cambridge, Miami and New York City–, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area

Musical recommendations and personalization in a social network

This paper presents a set of algorithms used for music recommendations and personalization in a general purpose social network www.ok.ru, the second largest social network in the CIS visited by more then 40 millions users per day. In addition to classical recommendation features like "recommend a sequence" and "find similar items" the paper describes novel algorithms for construction of context aware recommendations, personalization of the service, handling of the cold-start problem, and more. All algorithms described in the paper are working on-line and are able to detect and address changes in the user's behavior and needs in the real time. The core component of the algorithms is a taste graph containing information about different entities (users, tracks, artists, etc.) and relations between them (for example, user A likes song B with certainty X, track B created by artist C, artist C is similar to artist D with certainty Y and so on). Using the graph it is possible to select tracks a user would most probably like, to arrange them in a way that they match each other well, to estimate which items from a fixed list are most relevant for the user, and more. In addition, the paper describes the approach used to estimate algorithms efficiency and analyze the impact of different recommendation related features on the users' behavior and overall activity at the service.Comment: This is a full version of a 4 pages article published at ACM RecSys 201