A Novel Similarity Measure for Fuzzy Recommender Systems

Due to the development of the internet and the diversity of information, decision making in various fields has been faced different challenges. Recommender systems by identifying users interests, data filtering, and data management offer personalized services to users. This is beneficial for marketing and user satisfaction. Collaborative Filtering (CF) is one of the most successful methods of recommender system. CF is based on the similarity between users. We argue that similarity is a fuzzy notion and we get more realistic results in recommender systems by using fuzzy logic. Fuzzy logic is an effective way to identify ambiguities and uncertainty in measuring the similarity of items and users. In this paper, we present a new Fuzzy Similarity Measure, called FSM, for CF recommender systems which is based on popularity and significance. To evaluate the contribution of this work, we use MAE, F1, recall, and precision. Using the proposed fuzzy similarity measure, FSM, we obtain a F1 value equal to 0.6550, which outperforms the PIP and NHSM respectively by %17 and %20. Also, the MAE value based on the proposed fuzzy similarity measure is equal to 0.4604, which outperforms the NHSM by %5. We have also observed an improvement in recall and precision using the proposed similarity measure.