Perbandingan Metode Collaborative Filtering dan Hybrid Semantic Similarity
نویسندگان
چکیده
منابع مشابه
Use of Semantic Similarity and Web Usage Mining to Alleviate the Drawbacks of User-Based Collaborative Filtering Recommender Systems
One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-ba...
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recommender systems (rs) provide personalized recommendation according to user need by analyzing behavior of users and gathering their information. one of the algorithms used in recommender systems is user-based collaborative filtering (cf) method. the idea is that if users have similar preferences in the past, they will probably have similar preferences in the future. the important part of col...
متن کاملSemantic Ratings and Heuristic Similarity for Collaborative Filtering
Collaborative filtering systems make recommendations based on ratings of user preference. Usually, the ratings are unidimensional (e.g. like vs. dislike), and can either explicitly elicited from users or, more typically, are implicitly generated from observations of user behavior. This research examines multi-dimensional or semantic ratings in which a system gets information about the reason be...
متن کاملA New Similarity Measure Based on Item Proximity and Closeness for Collaborative Filtering Recommendation
Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...
متن کاملA NOVEL FUZZY-BASED SIMILARITY MEASURE FOR COLLABORATIVE FILTERING TO ALLEVIATE THE SPARSITY PROBLEM
Memory-based collaborative filtering is the most popular approach to build recommender systems. Despite its success in many applications, it still suffers from several major limitations, including data sparsity. Sparse data affect the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure based...
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ژورنال
عنوان ژورنال: Jurnal Nasional Teknologi Terapan (JNTT)
سال: 2019
ISSN: 2615-5877,2613-9235
DOI: 10.22146/jntt.44938