Personalizing information retrieval: A new model for user preferences elicitation

Fakhfakh, Rim; Feki, Ghada; Ammar, Anis Ben; Amar, Chokri Ben

doi:10.1109/smc.2016.7844548

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…The most relevant document matching to user"s objectives is given as result. User"s social web interactions are explored to extract his preferences and interests and this information is used to build the user profile and search personalization is done using this built user profile in [3]. A vector of terms corresponding to user interest is created and this represents the user profile.…”

Section: Related Workmentioning

confidence: 99%

Personalization in Collaborative Fusion based Enterprise Information retrieval

L*,

2019

IJRTE

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Due to data spread across various heterogeneous data stores, information retrieval in Enterprise data stores is always challenging compared to web based retrieval systems. We have proposed a collaborative fusion based information retrieval in [1] using the observation on similar users tends to prefer similar search results. The solution applied three dimensions of user similarity, document similarity and user to documents affinity to a collaborative information fusion based retrieval. The work also proposed active feedback based search result revision to get highly relevant results. But the work did not have any provision for personalization and could not handle the cold start problems. Without consideration for cold start problems, the user to document affinity cannot be modeled accurately as the result, the collaborative fusion process is affected. In this work, we improve our earlier solution of collaborative fusion based information retrieval with consideration for user personalization and solution for cold start problems. The solution is based on query refinement using the information hidden in enterprise messaging systems. A user profile is built as vector of concepts using the information in enterprise messaging systems and this user profile concept vector is used to refine the query in way to personalize the results and avoid cold start problems. Compared to approach in [1], the proposed query refinement based personalization is able to increase the relevancy accuracy by 10% as obtained from experimental results.

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Section: Related Workmentioning

confidence: 99%

Personalization in Collaborative Fusion based Enterprise Information retrieval

L*,

2019

IJRTE

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“…Treated items can be texts, images or videos. This profile is used to identify new interesting items for the user which is relevant to his profile [42], [43]. The recommendation model is based on the comparison between items and users features.…”

Section: Content-based Recommendationmentioning

confidence: 99%

Deep Learning-Based Recommendation: Current Issues and Challenges

Fakhfakh¹,

Ammar²,

Amar³

2017

ijacsa

Self Cite

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Abstract-Due to the revolutionary advances of deep learning achieved in the field of computer vision, object recognition and natural language processing, the deep learning gained much attention. The recommendation task is influenced by the deep learning trend which shows its significant effectiveness. The deep learning based recommender models provides a better detention of user preferences, item features and users-items interactions history. In this paper, we provide a recent literature review of researches dealing with deep learning based recommendation approaches which is preceded by a presentation of the main lines of the recommendation approaches and the deep learning techniques. Then we finish by presenting the recommendation approach adopted by the most popular video recommendation platform YouTube which is based essentially on deep learning advances.

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“…The RS recommends items which are similar to the ones that the user implicitly or explicitly interacts with previously through rating or clicking. Hence, a user profile is created and used to identify new interesting and relevant items for the user (Fakhfakh et al, 2016;.…”

Section: Introductionmentioning

confidence: 99%

A Deep Autoencoder-Based Hybrid Recommender System

Bougteb

Ouhbi

Frikh³

et al. 2022

International Journal of Mobile Computing and Multimedia Communications

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Recommender systems build their suggestions on rating data, given explicitly or implicitly by users on items. These ratings create a huge sparse user-item rating matrix which opens two challenges for researchers on the field. The first challenge is the sparsity of the rating matrix and the second one is the scalability of the data. This article proposes a hybrid recommender system based on deep autoencoder to learn the user interests and reconstruct the missing ratings. Then, SVD++ decomposition is employed, in parallel, to hold information of correlation between different features factors. Additionally, the authors gave a deep analysis of the top-N recommender list from the user's perspective to ensure that the proposed model can be used for practical application. Experiments showed that the proposed model performed better with high-dimensional datasets, and outperformed various hybrid algorithms in terms of RMSE, MAE, and in terms of the final top-N recommendation list.

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Personalizing information retrieval: A new model for user preferences elicitation

Cited by 7 publications

References 16 publications

Personalization in Collaborative Fusion based Enterprise Information retrieval

Personalization in Collaborative Fusion based Enterprise Information retrieval

Deep Learning-Based Recommendation: Current Issues and Challenges

A Deep Autoencoder-Based Hybrid Recommender System

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