Multiobjective Pareto-Efficient Approaches for Recommender Systems

Abstract

Recommender systems are quickly becoming ubiquitous in applications such as e-commerce, social media channels, and content providers, among others, acting as an enabling mechanism designed to overcome the information overload problem by improving browsing and consumption experience. A typical task in many recommender systems is to output a ranked list of items, so that items placed higher in the rank are more likely to be interesting to the users. Interestingness measures include how accurate, novel, and diverse are the suggested items, and the objective is usually to produce ranked lists optimizing one of these measures. Suggesting items that are simultaneously accurate, novel, and diverse is much more challenging, since this may lead to a conflicting-objective problem, in which the attempt to improve a measure further may result in worsening other measures. In this article, we propose new approaches for multiobjective recommender systems based on the concept of Pareto efficiency—a state achieved when the system is devised in the most efficient manner in the sense that there is no way to improve one of the objectives without making any other objective worse off. Given that existing multiobjective recommendation algorithms differ in their level of accuracy, diversity, and novelty, we exploit the Pareto-efficiency concept in two distinct manners: (i) the aggregation of ranked lists produced by existing algorithms into a single one, which we call Pareto-efficient ranking, and (ii) the weighted combination of existing algorithms resulting in a hybrid one, which we call Pareto-efficient hybridization. Our evaluation involves two real application scenarios: music recommendation with implicit feedback (i.e., Last.fm) and movie recommendation with explicit feedback (i.e., MovieLens). We show that the proposed Pareto-efficient approaches are effective in suggesting items that are likely to be simultaneously accurate, diverse, and novel. We discuss scenarios where the system achieves high levels of diversity and novelty without compromising its accuracy. Further, comparison against multiobjective baselines reveals improvements in terms of accuracy (from 10.4% to 10.9%), novelty (from 5.7% to 7.5%), and diversity (from 1.6% to 4.2%).

How good your recommender system is? A survey on evaluations in recommendation

“…We emphasize the need of a novel work which can effectively provide user satisfaction evaluations for the state-ofart recommenders under the same evaluation scenario and datasets considering the mentioned concepts. Moreover, in order to complement such research, such work could also analyse the impacts of attempting to evaluate and optimize all of those concepts simultaneously and attempt to combine recommendations with different objectives, similar to what was performed by Zhang et al [38] and Ribeiro et al [28] in a limited level.…”

“…Moreover, future works could consider designing algorithms that simultaneously balance the surveyed concepts in a single recommendation list. Zhang et al [38] attempted to balance novelty, diversity and serendipity, while Ribeiro et al [28] attempted improving accuracy, novelty and diversity, accordingly. Nevertheless, novel attempts in studying and balancing all of the mentioned concepts are necessary for increasing user satisfaction considering the needs and desires of the user.…”

Understanding the role of latent feature diversification on choice difficulty and satisfaction

“…Their diversification was based on item-item similarity calculated from rating patterns. Recently Ribeiro et al (2014) also proposed a diversification method using item similarity based on rating patterns. A simulation showed that their diversification method results in recommendations that are simultaneously accurate, diverse and novel.…”

Online Social Media Recommendation Over Streams

“…In [18], the trade-off between diversity and matching quality is formulated as a binary optimization problem, and the diversity level can be explicitly tuned. In [25], the recommendation is treated as a multi-objective problem that combines several recommendation methods in a way of maximizing the diversity. Puthiya Parambath et al [23] represent the items as a similarity graph, and conduct recommendation by finding a small set of unrated items that best covers a subset of items positively rated by the user.…”