Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Personalized Item Ranking from Implicit User Feedback: A Heterogeneous Information Network Approach

In today’s era of the digital world with information overload, generating personalized recommendations for the e-commerce users is a challenging and interesting problem. Recommendation of top-N items of interest to a user of e-commerce is highly challenging using binary implicit feedback. The training data is usually very sparse and have binary values capturing a user’s action or inaction. Due to the sparseness of data and lack of explicit user preferences, the recommendations generated by model-based and neighborhood-based approaches are not effective. Of late, network-based item recommendation methods, which utilize item related meta-information, are beginning to attract increasing attention for binary implicit feedback data. In this work, we propose a heterogeneous information network based recommendation model for personalized top-N recommendations using binary implicit feedback data. To utilize the potential of meta-information related to items, we utilize the concept of meta-path. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various meta-paths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback data. To show the effectiveness, the proposed model is experimentally evaluated using the real-world dataset. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/3

The effect of heterogeneous dynamics of online users on information filtering

The rapid expansion of the Internet requires effective information filtering techniques to extract the most essential and relevant information for online users. Many recommendation algorithms have been proposed to predict the future items that a given user might be interested in. However, there is an important issue that has always been ignored so far in related works, namely the heterogeneous dynamics of online users. The interest of active users changes more often than that of less active users, which asks for different update frequency of their recommendation lists. In this paper, we develop a framework to study the effect of heterogeneous dynamics of users on the recommendation performance. We find that the personalized application of recommendation algorithms results in remarkable improvement in the recommendation accuracy and diversity. Our findings may help online retailers make better use of the existing recommendation methods