Accurate and Novel Recommendations

Amin Javari, Mahdi Jalili
2014 ACM Transactions on Intelligent Systems and Technology  
Recommender systems are in the center of network science and becoming increasingly important in individual businesses for providing efficient personalized services and products to users. The focus of previous research in the field of recommendation systems was on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation list as key characteristics of modern recommender
more » ... ystems. In many cases, novelty and precision do not go in the same direction and the accuracy-novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a trade-off between them. In this work, we propose an algorithm for providing novel and accurate recommendation to users. We consider the standard definition of accuracy and an effective self-information based measure to assess novelty of the recommendation list. The proposed algorithm is based on item popularity, which is defined as the number of votes they receive in a certain time interval. Wavelet transform is used for analyzing popularity time series and forecasting their trend in future time steps. We introduce two filtering algorithms based on the information extracted from analyzing popularity time series of the items. Popularity-based filtering algorithm, gives higher chance to items which are predicted to be popular in future time steps. The other algorithm, denoted as novelty and population based filtering algorithm, is to move towards items with low popularity in past time steps that are predicted to become popular in the future. The introduced filters can be applied as adds-on to any recommendation algorithm. In this paper, we use the proposed algorithms to improve the performance of classic recommenders including item-based collaborative filtering and Markov-based recommender systems. The experiments show that the algorithms could significantly improve both the accuracy and effective novelty of the classic recommenders.
doi:10.1145/2668107 fatcat:wxjd6os4dnhmhhovkahuzv6npu