Content-based Collaborative Filtering Research Articles

Which Influencers Can Maximize PCR of E-Commerce?

The Web has provided an increasing proportion of use as a medium for e-commerce in addition to various recommender systems. It can be used for analyzing recommendation system-based feedback (e.g., a form in which a user inputs their preferences for various items as numerical values into a specific evaluation system) to estimate customer interest; in addition, analyzing multi-modal types of feedback (e.g., product purchase traces, inquiry lists, inquiry times, and comments) with deep learning can also be used to estimate user interest. As many companies around the world promote their products through micro-influencers on the Web, related research has continued to predict the purchase conversion rate of the influencer through a variety of technologies. In this work, we present a multi-modal micro-influencer analysis scheme for a marketing maximization strategy. Our scheme uses the multi-modal data stored in Mecha Solution’s own shopping mall of Korea, as well as famous Korean Internet platforms, and Coupang, Naver, and Oliveyoung’s data such as article posting comments and statistics information. By extracting the main characteristics of the real article postings from real users as opposed to those from factitious influencers posting articles and comments and identifying articles other than advertisements, influencer scores are obtained, assuming that articles other than advertisements can further increase the purchase conversion rate. Based on influencer score, we propose a multi-modal micro-influencer analysis scheme that recommends influencers use content-based collaborative filtering and user-based collaborative filtering for items that the influencer has not yet reviewed. The experiment was implemented to prove that the proposed scheme successfully achieves this goal.

AI in Video Recommendation System

Short videos are very popular all over the world. Video recommendation system is an essential part in it. It can help people to watch the video that they are interested in. This paper is written for study the specific principle of the video recommendation system. The result was getting through relative literatures and actual test. Short video recommendation systems typically use collaborative filtering and deep learning techniques to achieve this. Collaborative filtering comes in two types: user-based and content-based. User-based collaborative filtering recommends videos to new users based on the viewing behavior of similar users. Content-based collaborative filtering uses video features and similarity to recommend similar videos. Finally, this paper shows how the video web set can learn what is the user’s interest.

Content-Based Collaborative Filtering with Hierarchical Agglomerative Clustering Using User/ Item based Ratings

The recommender system (RS) plays the major role in online networks, online shopping, and online services etc. The conventional RSs are suffering with the inaccurate quality of experience to the users, so the improper content is recommending to customers. The content based collaborative filtering (CBCF) method is introduced to solve the issues presented in the RSs. However, the CBCF method is suffering with the cold start problem for new users and suffering with data accuracy, data sparsity, and scalable data in clustering process. Thus, to solve these problems, this article proposes hierarchical agglomerative clustering (HAC) based collaborative filtering (HAC-CF) for RSs. The proposed HAC-CF based RS functions by utilizing the incentivized/ penalized user (IPU) model with user-based and item-based ratings. To this end, users are divided into several clusters through single link graph partitioning through minimum distance criteria. Then, the final item ranking is computed using Pearson correlation coefficient (PCC) similarity of users. Hence, recommendation efficiency and accuracy are increased at the end user by combining user, item models. The simulation results show the performance enhancement of proposed method with respect to F1-score, recall, and precision as compared to the conventional approaches.

An Algorithm for Generating a Recommended Rule Set Based on Learner's Browse Interest

To personalize the recommended learning information according to the interests of the learner, a recommendation rule set generation algorithm based on learner browsing interests was proposed. First, the learner's browsing behavior was captured. A multivariate regression method was used to calculate the quantitative relationship between the learner's browsing behavior and the degree of interest in the web page to generate a learner's current interest view (CIV). With this current interest view, a content-based collaborative filtering personalized information recommendation service was provided to learners. Then, a new weighted association rule algorithm was used to discover the associations between the items, so that the degree of recommendation was obtained. Furthermore, the degree of recommendation was used as a personalized recommendation service for learners with long-term interests. The results showed that the proposed algorithm effectively improved the quality of information recommendation and the real-time performance of the recommendation. Therefore, this algorithm has a good application value in the field of personalized learning recommendation.

Goal-based hybrid filtering for user-to-user Personalized Recommendation

Recommendation systems are gaining great importance with e-Learning and multimedia on the internet. It fails in some situations such as new-user profile (cold-start) issue. To overcome this issue, we propose a novel goalbased hybrid approach for user-to-user personalized similarity recommendation and present its performance accuracy. This work also helps to improve collaborative filtering using k-nearest neighbor as neighborhood collaborative filtering (NCF) and content-based filtering as content-based collaborative filtering (CBCF). The purpose of combining k-nn with recommendation approaches is to increase the relevant recommendation accuracy and decrease the new-user profile (cold-start) issue. The proposed goal-based approach associated with nearest neighbors, compare personalized profile preferences and get the similarities between users. The paper discussed research architecture, working of proposed goal-based approach, its experimental steps and initial results. DOI: http://dx.doi.org/10.11591/ijece.v3i3.2419

Related, but not Relevant: Content-Based Collaborative Filtering in TREC-8

Historically, solutions to the TREC filtering tasks have focused exclusively on the content of documents and search topic descriptions as training data. These approaches are well-known for their ability to focus on those salient concepts in the document stream which are most useful for separating relevant documents from irrelevant ones. However, one kind of information that has not been used is the relationships among the topics themselves. In our TREC-8 routing experiments, we employed a collaborative (or social) filtering algorithm, based on latent semantic indexing which highlights common term usage patterns among groups of filtering profiles. Our hypothesis was that this would allow related topics to share common relevant documents. We found, however, that the algorithm also recommends many documents of related, yet irrelevant interest. As a result of this process, many similar search topics are “linked” together by common sets of documents recommended to them. We visualize these topic relationships using graphs where topics are nodes and edges exist where two topics share a recommended document.