Towards a Semantic Graph-based Recommender System. A Case Study of Cultural Heritage

Research on digital cultural heritage has raised the importance of providing visitors with relevant assistance before and during their visits. With the advent of the social web, the cultural heritage area is affected by the problem of information overload. Indeed, a large number of available resources have emerged coming from the social information systems (SocIS). Therefore, visitors are swamped with enormous choices in their visited cities. SocIS platforms use the features of collaborative tagging, named folksonomy, to commonly contribute to the management of the shared resources. However, collaborative tagging uses uncontrolled vocabulary which semanti- cally weakens the description of resources, consequently decreases their classification, clustering, thereby their recommendation. Therefore, the shared resources have to be pertinently described to ameliorate their recommendations. In this paper, we aim to enhance the cultural heritage visits by suggesting semantically related places that are most likely to interest a visitor. Our proposed approach represents a semantic graph-based recommender system of cultural heritage places through two steps; (1) constructing an emergent semantic description that semantically augments the place and (2) effectively modeling the emerging graphs representing the semantic relatedness of similar cultural heritage places and their related tags. The experimental evaluation shows relevant results attesting the efficiency of the proposed approach.

A method of query expansion based on topic models and user profile for search in folksonomy

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

Expert Detection and Recommendation Model With User-Generated Tags in Collaborative Tagging Systems

Tags generated in collaborative tagging systems (CTSs) may help users describe, categorize, search, discover, and navigate content, whereas the difficulty is how to go beyond the information explosion and obtain experts and the required information quickly and accurately. This paper proposes an expert detection and recommendation (EDAR) model based on semantics of tags; the framework consists of community detection and EDAR. Specifically, this paper firstly mines communities based on an improved agglomerative hierarchical clustering (I-AHC) to cluster tags and then presents a community expert detection (CED) algorithm for identifying community experts, and finally, an expert recommendation algorithm is proposed based the improved collaborative filtering (CF) algorithm to recommend relevant experts for the target user. Experiments are carried out on real world datasets, and the results from data experiments and user evaluations have shown that the proposed model can provide excellent performance compared to the benchmark method.

Recommender Systems Using Collaborative Tagging

Collaborative tagging is a useful and effective way for classifying items with respect to search, sharing information so that users can be tagged via online social networking. This article proposes a novel recommender system for collaborative tagging in which the genre interestingness measure and gradual decay are utilized with diffusion similarity. The comparison has been done on the benchmark recommender system datasets namely MovieLens, Amazon datasets against the existing approaches such as collaborative filtering based on tagging using E-FCM, and E-GK clustering algorithms, hybrid recommender systems based on tagging using GA and collaborative tagging using incremental clustering with trust. The experimental results ensure that the proposed approach achieves maximum prediction accuracy ratio of 9.25% for average of various splits data of 100 users, which is higher than the existing approaches obtained only prediction accuracy of 5.76%.

Enhance the Quality of Collaborative Filtering using Tagging

Background: Due to enormous data in web sites, recommending users for every item is impossible. For this problem Recommender Systems (RS) are introduced. RS is categorized into content-based (CB), collaborative Filtering (CF) and Hybrid RS. Based on these techniques recommendations are done to user. In this, CF is the recent technique used in RS in which tagging features also provided. Objective: Three main issues occur in RS are scalability problem which occurs when there is a huge data, sparsity problem occurs when rating data is missing and cols start user or item problem occurs when new user or new item enters in the system. To avoid these issues here we have proposed Tag and Time weight model with GA in Collaborative Tagging. Method: Here we have proposed a method Collaborative Tagging (CT) with Tag and Time weight model with real value genetic algorithm which enhances the recommendation quality by removing the issues of sparsity and cold start user problems with the help of missing value prediction. Here in this the sparsity problem can be removed using missing value prediction and cold start problems are removed using tag and time weight model using GA. Results: Here we have compared the results of Collaborative Filtering with cosine similarity (CF-CS), Collaborative Filtering with Diffusion Similarity (CF-DS), Tag and Time weight model with Diffusion similarity (TAW-TIW-DS) and Tag and Time weight model using Diffusion similarity and Genetic algorithm (TAW-TIW-DS-GA). Conclusion: Here we have compare the proposed approach with the baseline approaches and the metrics are used MAE, prediction percentage, Hit-rate and Hit-rank. Based on these metrics for every split TAW-TIW-DS-GA shown best results as compared to existing approach.

Improving Scalability, Sparsity and Cold Start user Issues in Collaborative Tagging with Incremental Clustering and Trust

: As web sites grows the complexity increases in many websites due to huge data. To maintain these data it is very difficult because there are many number of users are increasing day by day. As per information of many websites, there is very insufficient data to get the accuracy or efficiency of web sites. To improve the quality of websites, Recommender Systems are introduced. On the basis of these recommender systems, user gives the ratings to an item and then the reviews are generated for each an item so that the user might know the information of items relevant to his preferences. Here the RS is classified into content-based and collaborative filtering. Later tagging also included in this collaborative filtering. The main issues of Collaborative Filtering are Scalability, cold-start user and sparsity problems. We propose and explore the benefits of collaborative filtering based on tagging for sparseness, scalability and Cold start user issues.