Hossein Rahmatizadeh Zagheli
Hamed Zamani
ABSTRACT
Content-based recommender systems (CBRSs) rely on user-item similarities that are calculated between user profiles and item representations. Appropriate representation of each user profile based on the user's past preferences can have a great impact on user's satisfaction in CBRSs. In this paper, we focus on text recommendation and propose a novel profile updating model based on previously recommended items as well as semantic similarity of terms calculated using distributed representation of words. We evaluate our model using two standard text recommendation datasets: TREC-9 Filtering Track and CLEF 2008-09 INFILE Track collections. Our experiments investigate the importance of both past recommended items and semantic similarities in recommendation performance. The proposed profile updating method significantly outperforms the baselines, which confirms the importance of incorporating semantic similarities in the profile updating task.
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Index Terms
- A Semantic-Aware Profile Updating Model for Text Recommendation
Recommendations
A semantic enhanced hybrid recommendation approach
Recommender systems are effectively used as a personalized information filtering technology to automatically predict and identify a set of interesting items on behalf of users according to their personal needs and preferences. Collaborative Filtering (...
An angle-based interest model for text recommendation
Building an interest model is the key to realize personalized text recommendation. Previous interest models neglect the fact that a user may have multiple angles of interest. Different angles of interest provide different requests and criteria for text ...
Learning Item/User Vectors from Comments for Collaborative Recommendation
ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and ComputingCollaborative Filtering (CF) has been widely used in many recommender systems over the past decades. Conventional CF-based methods mainly consider the ratings given to items via users and suffer from the sparsity and cold-start problems very much. ...
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