A Time-Aware Recommender System Based on Dependency Network of Items

Abstract

Recommender systems have been accompanied by many applications in both academia and industry. Among different algorithms used to construct a recommender system, collaborative filtering methods have attracted much attention and been used in many commercial applications. Incorporating the time into the recommendation algorithm can greatly enhance its performance. In this paper, we propose a novel time-aware model-based recommendation system. We show that future ratings of a user can be inferred from his/her rating history. We assume that there is cascade of information between the items such that rating an item can lead to other items being rated. There is indeed a hidden network structure among the items and each user tracks a sequence of items in this network. The dependencies between the items are modeled based on statistical diffusion models and the parameters are obtained through maximum-likelihood estimation. We show that under some mild assumptions, the estimation task becomes a convex optimization problem. A major advantage of the proposed method over classical recommender systems is the ability to include novel items in its recommendation lists besides providing accurate recommendations. The proposed model also results in personalized and diverse recommendations. Experimental evaluations show that the model can be trained based on the ratings of a limited number of users. Furthermore, the proposed model outperforms classical recommendation algorithms in terms of both accuracy and novelty.

Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships

“…Assuming that the recommendation system includes m customers and n commodities, R m×n � [R ij ] m×n represents the customercommodity scoring matrix, which is shown as Figure 1. R ij represents the rating of customer i to item j, where R ij ∈ [1,5]. Usually, there many empty elements in R m×n , and it will cause a sparse matrix of customer-commodity scoring.…”

“…At present, the recommender system had been widely used in different industries, such as Amazon product recommendation, iTunes music recommendation, and Netflix movie recommendation because the recommendation algorithm can filter according to the mass of user history information, mine the deep relationship between users and users or items, and produce more accurate personalized recommendation with preference characteristics, which can better meet the needs of users. e algorithms used by the recommendation system consist of three types: collaborative filtering recommendation algorithms [3,4], content-based recommendation algorithms [5], and hybrid recommendation algorithms [6]. Among them, collaborative filtering recommendation algorithm is currently the most popular, and it consists of three types: item-based collaborative filtering [7], user-based collaborative filtering [8], and matrix factorization collaborative filtering [9].…”

Ant Collaborative Filtering Addressing Sparsity and Temporal Effects

“…Other recent approaches dealing with time effect include Hidden Markov Chain [57], fusion model [52], probabilistic model [27], latent factor [56]. Among these methods, [5]'s work is similar to our idea in that it assumes a hidden network structure exists among the items, and each user tracks a sequence of items in this network. The difference is that in their work, the dependencies between the items are modelled based on statistical diffusion models, and the parameters are obtained through the maximum-likelihood estimation.…”

The essential role of time in network-based recommendation

“…The goal of recommendation is to use data on past user preferences to obtain personalized "recommendation" of new items (shopping items, YouTube videos, or any other content) that an individual user might appreciate. From the physics perspective, it has been interesting to realize that well-known physics processes, such as random walks and heat diffusion, on network representations [8] of the underlying data give rise to efficient recommendation methods [9][10][11][12].Despite physics being a science that aims at understanding the evolution of systems, the research of networkbased recommendation by physicists has entirely neglected the dimension of time which turns out to be of high importance for traditional recommendation approaches [13][14][15][16]. While this is understandable from the historical perspective-early datasets often lacked the time information-the situation is very different now.…”

“…Despite physics being a science that aims at understanding the evolution of systems, the research of networkbased recommendation by physicists has entirely neglected the dimension of time which turns out to be of high importance for traditional recommendation approaches [13][14][15][16]. While this is understandable from the historical perspective-early datasets often lacked the time information-the situation is very different now.…”