2 天之前 · Recommender systems have become a core component of various online platforms, helping users get relevant information from the abundant digital data. Traditional RSs often generate static recommendations, which may not adapt well to changing user preferences. To address this problem, we propose a novel reinforcement learning (RL) recommendation …

3 天之前 · Recommendation system is an effective way to alleviate information explosion [1].By combining a series of features of users and items and calculating the predicted score based on the metric method, the recommendation system filters and extracts the content preferred by users from a large amount of redundant information, thereby helping users to efficiently lock in the …

4 天之前 · This paper provides a comprehensive overview of state-of-the-art systems and a case study of a recommender system for a small, low-budget project, battling with several constraints: the use of uncommon natural language, the use of raw, unlabelled textual data, thus using mainly techniques coming from the field of data mining and text mining ...

2 天之前 · In Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 452–461. Liu et al. (2023) Weiwen Liu, Wei Guo, Yong Liu, Ruiming Tang, and Hao Wang. 2023. User Behavior Modeling with Deep Learning for Recommendation: Recent Advances. In Proceedings of the 17th ACM Conference on Recommender Systems. …

2025年2月15日 · Recommender systems (RSs) have widespread applications in fields such as e-commerce, social media, and entertainment. Their primary goal is to recommend content that users may find interesting by analyzing user historical behavior data and item characteristics [1, 2].Traditional recommender system methods include collaborative filtering, content-based …

This survey paper provides a comprehensive analysis of the evolution and current landscape of recommendation systems, extensively used across various web applications. It categorizes recommendation techniques into four main types: content-based, collaborative filtering, knowledge-based, and hybrid approaches, tailored for specific user contexts.

Abstract: Rapid data expansion brought on by the emergence of the Web and e-commerce websites has led to the issue of information overload. Recommendation systems (RSs) have played a significant role in addressing this issue by using different algorithms to analyze user behavior and preferences and use this information to provide personalized ...

2010年10月20日 · We have discussed the motivations of such kinds of techniques, the algorithmic issues, and the experimental studies as well as the insightful findings and results. In this chapter, we will shift to another important application of Web data mining: Web recommendation.

The book discusses how to measure the effectiveness of recommender systems, illustrating the methods with practical case studies. It strikes a balance between fundamental concepts and state-of-the-art technologies, providing readers with …

This paper presents a new model for developing a collaborative web recommendation system using a new technique for knowledge extraction. The proposed model introduces two techniques: cluster similarity-based technique and rule extraction technique to provide proper recommendations that meet the user’s needs.