Presenting a hybrid recommender system based on collaborative filtering techniques and item image content

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Article Type:
Research/Original Article (بدون رتبه معتبر)
Abstract:

Recommender systems, with the ability to recognize the user and predict his priorities, sift through the information that is likely to be of interest to the user from the large volume of data and save the user's time and energy by recommending them to the user. On the other hand, these systems, with the ability to analyze and store the user's past behaviors, also infer existing services and information that the user has not paid attention to but is probably interested in and provide interesting results to users in the form of recommendations. While in the vast majority of works done in the field of recommender systems, each item is displayed only with numerical or string features, one of the features that has a significant impact on determining the level of desirability of items from the users' point of view is their images. This is more important for products such as clothing, jewelry, etc., which are mostly accepted by customers due to their physical appearance. For this purpose, this paper aims to present a combined method to increase the efficiency of these systems using collaborative filtering techniques and the content of the images of the items. In this paper, first, using image processing techniques, the visual features of the items are extracted, then by selecting the appropriate similarity criterion and using the technical specifications of the items and the individual characteristics of the users, the users and similar items will be segmented. After that, using this information and utilizing the collaborative filtering technique, the best suggestions that are most similar to the users' tastes will be presented. Experimental results show that the proposed method has been able to demonstrate appropriate efficiency.

Language:
Persian
Published:
Journal of New Achievements in Electrical, Computer and Technology, Volume:4 Issue: 4, 2025
Pages:
89 to 135
https://www.magiran.com/p2817523