Learning Word Embeddings from Wikipedia for Content-Based Recommender Systems

Exploring the effects of Clustering Algorithms on Free Text Recommendation

10.20944/preprints201811.0172.v1 ◽

2018 ◽

Author(s):

Israel Mendonca dos Santos ◽

Antoine Trouve ◽

Akira Fukuda ◽

Kazuaki Murakami

Keyword(s):

Recommender Systems ◽

Recommender System ◽

Negative Impact ◽

Clustering Algorithms ◽

Search Space ◽

Free Text ◽

Word Embeddings ◽

Performance Measurements ◽

Number Of Clusters

In this paper, we provide a study on the effects of applying classical clustering algorithms, such as k-Means to free text recommender systems. A typical recommender system may face problems when the number of items from a database goes from a few items to hundreds of items. Currently, one of the most prominent techniques to scale the database is applying clustering, however clustering may have a negative impact on the accuracy of the system when applied without taking into consideration the underlying items. In this work, we build a conceptual text recommender system and use k-Means to partition its search space into different groups. We study how the variation of the number of clusters affects its performance in the light of two performance measurements: recommendation time and precision. We also analyze if this clustering is affected by the representation of text we use. All the techniques used in this study uses word-embeddings to represent the document. One of the main findings of this work is that using clustering we can improve the recommendation time in up to almost 30 times without affecting much off its initial accuracy. Another interesting finding is that the increment of the number of clusters is not directly translated into linear performance.

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Group User Profile Modeling Based on Neural Word Embeddings in Social Networks

Symmetry ◽

10.3390/sym10100435 ◽

2018 ◽

Vol 10 (10) ◽

pp. 435 ◽

Cited By ~ 3

Author(s):

Jianxing Zheng ◽

Deyu Li ◽

Sangaiah Arun Kumar

Keyword(s):

Recommender Systems ◽

User Profile ◽

Diverse Group ◽

Word Embeddings ◽

User Profiles ◽

Sina Weibo ◽

The Social ◽

Research Problems ◽

Fundamental Research ◽

Profile Vector

How to find a user’s interest from similar users a fundamental research problems in socialized recommender systems. Despite significant advances, there exists diversity loss for the majority of recommender systems. With this paper, for expanding the user’s interest, we overcome this challenge by using representative and diverse similar users from followees. First, we model a personal user profile vector via word2vec and term frequency mechanisms. According to user profiles and their follow relationships, we compute content interaction similarity and follow interaction similarity. Second, by combining two kinds of interaction similarity, we calculate the social similarity and discover a diverse group with coverage and dissimilarity. The users in a diverse group can distinguish each other and cover the whole followees, which can model a group user profile (GUP). Then, by tracking the changes of followee set, we heuristically adjust the number of diverse group users and construct an adaptive GUP. Finally, we conduct experiments on Sina Weibo datasets for recommendation, and the experimental results demonstrate that the proposed GUP outperforms conventional approaches for diverse recommendation.

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Improving Recommender Systems Using Co-Appearing and Semantically Correlated User Interests

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190115162311 ◽

2020 ◽

Vol 13 (2) ◽

pp. 240-247 ◽

Cited By ~ 7

Author(s):

Bilal Hawashin ◽

Darah Aqel ◽

Shadi Alzubi ◽

Mohammad Elbes

Keyword(s):

Recommender Systems ◽

Experimental Work ◽

Execution Time ◽

Word Embeddings ◽

User Interest ◽

User Interests ◽

And Behavior ◽

The Given

Background: Recommender Systems use user interests to provide more accurate recommendations according to user actual interests and behavior. Methods: This work aims at improving recommender systems by discovering hidden user interests from the existing interests. User interest expansion would contribute in improving the accuracy of recommender systems by finding more user interests using the given ones. Two methods are proposed to perform the expansion: Expanding interests using correlated interests’ extractor and Expanding interests using word embeddings. Results: Experimental work shows that such expanding is efficient in terms of accuracy and execution time. Conclusion: Therefore, expanding user interests proved to be a promising step in the improvement of the recommender systems performance.

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