Towards Time-Aware Collaborative Filtering Recommendation System

Dawei Wang, Purdue University

Abstract

As technological capacity to store and exchange information progress, the amount of available data grows explosively, which can lead to information overload. The difficulty of making decisions effectively increases when one has too much information about that issue. Recommendation systems are a subclass of information filtering systems that aim to predict a user’s opinion or preference of topic or item, thereby providing personalized recommendations to users by exploiting historic data. They are widely used in e-commerce such as Amazon.com, online movie streaming companies such as Netflix, and social media networks such as Facebook. Memory-based collaborative filtering (CF) is one of the recommendation system methods used to predict a user’s rating or preference by exploring historic ratings, but without incorporating any content information about users or items. Many studies have been conducted on memory-based CFs to improve prediction accuracy, but none of them have achieved better prediction accuracy than state-of-the-art model-based CFs. Furthermore, A product or service is not judged only by its own characteristics but also by the characteristics of other products or services offered concurrently. It can also be judged by anchoring based on users’ memories. Rating or satisfaction is viewed as a function of the discrepancy or contrast between expected and obtained outcomes documented as contrast effects. Thus, a rating given to an item by a user is a comparative opinion based on the user’s past experiences. Therefore, the score of ratings can be affected by the sequence and time of ratings. However, in traditional CFs, pairwise similarities measured between items do not consider time factors such as the sequence of rating, which could introduce biases caused by contrast effects. In this research, we proposed a new approach that combines both structural and rating-based similarity measurement used in memory-based CFs. We found that memory-based CF using combined similarity measurement can achieve better prediction accuracy than model-based CFs in terms of lower MAE and reduce memory and time by using less neighbors than traditional memory-based CFs on MovieLens and Netflix datasets. We also proposed techniques to reduce the biases caused by those user comparing, anchoring and adjustment behaviors by introducing the time-aware similarity measurements used in memory-based CFs. At last, we introduced novel techniques to identify, quantify, and visualize user preference dynamics and how it could be used in generating dynamic recommendation lists that fits each user’s current preferences.

Degree

Ph.D.

Advisors

Ventresca, Purdue University.

Subject Area

Information science

COinS