A model for mobile content filtering on non-interactive recommendation systems

To overcome the problem of information overloading in mobile communication, a recommendation system can be used to help mobile device users. However, there are problems relating to sparsity of information from a first-time user in regard to initial rating of the content and the retrieval of relevant items. In order for the user to experience personalized content delivery via the mobile recommendation system, content filtering is necessary. This paper proposes an integrated method by using classification and association rule techniques for extracting knowledge from mobile content in a user's profile. The knowledge can be used to establish a model for new users and first rater on mobile content. The model recommends relevant content in the early stage during the connection based on the user's profile. The proposed method also facilitates association to be generated to link the first rater items to the top items identified from the outcomes of the classification and clustering processes. This can address the problem of sparsity in initial rating and new user's connection for non-interactive recommendation systems

Improved collaborative filtering using clustering and association rule mining on implicit data

The recommender systems are recently becoming more significant due to their ability in making decisions on appropriate choices. Collaborative Filtering (CF) is the most successful and most applied technique in the design of a recommender system where items to an active user will be recommended based on the past rating records from like-minded users. Unfortunately, CF may lead to poor recommendation when user ratings on items are very sparse (insufficient number of ratings) in comparison with the huge number of users and items in user-item matrix. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate users’ preferences by providing more evidences and information through observations made on users’ behaviors. Data mining technique, which is the focus of this research, can predict a user’s future behavior without item evaluation and can too, analyze his preferences. In order to investigate the states of research in CF and implicit feedback, a systematic literature review has been conducted on the published studies related to topic areas in CF and implicit feedback. To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). It uses item repetition within a transaction as an input for association rules mining, in which can achieve high recommendation accuracy. To do this, a modified preprocessing has been employed to discover similar interest patterns among users. In addition, the clustering technique (Hierarchical clustering) has been used to reduce the size of data and dimensionality of the item space as the performance of association rules mining. Then, similarities between items based on their features have been computed to make recommendations. Experiments have been conducted and the results have been compared with basic CF and other extended version of CF techniques including K-Means Clustering, Hybrid Representation, and Probabilistic Learning by using public dataset, namely, Million Song dataset. The experimental results demonstrate that the proposed technique exhibits improvements of an average of 20% in terms of Precision, Recall and Fmeasure metrics when compared to the basic CF technique. Our technique achieves even better performance (an average of 15% improvement in terms of Precision and Recall metrics) when compared to the other extended version of CF techniques, even when the data is very sparse

An integrated mobile content recommendation system

Many features have been added to mobile devices to assist the user's information consumption. However, there are limitations due to information overload on the devices, hardware usability and capacity. As a result, content filtering in a mobile recommendation system plays a vital role in the solution to this problem. A system that utilises content filtering can recommend content which matches a user's needs based on user preferences with a higher accuracy rate. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. The problems can be viewed as first time connection and first content rating for non-interactive recommendation systems where information is insufficient to predict mobile content which will match with a user's needs. In addition, how to find relevant items for the content recommendation system which are related to a user's profile is also a concern. An integrated model that combines the user group identification and mobile content filtering for mobile content recommendation was proposed in this study in order to address the current limitations of the mobile content recommendation system. The model enhances the system by finding the relevant content items that match with a user's needs based on the user's profile. A prototype of the client-side user profile modelling is also developed to demonstrate the concept. The integrated model applies clustering techniques to determine groups of users. The content filtering implemented classification techniques to predict the top content items. After that, an adaptive association rules technique was performed to find relevant content items. These approaches can help to build the integrated model. Experimental results have demonstrated that the proposed integrated model performs better than the comparable techniques such as association rules and collaborative filtering. These techniques have been used in several recommendation systems. The integrated model performed better in terms of finding relevant content items which obtained higher accuracy rate of content prediction and predicted successful recommended relevant content measured by recommendation metrics. The model also performed better in terms of rules generation and content recommendation generation. Verification of the proposed model was based on real world practical data. A prototype mobile content recommendation system with client-side user profile has been developed to handle the revisiting user issue. In addition, context information, such as time-of-day and time-of-week, could also be used to enhance the system by recommending the related content to users during different time periods. Finally, it was shown that the proposed method implemented fewer rules to generate recommendation for mobile content users and it took less processing time. This seems to overcome the problems of first time connection and first content rating for non-interactive recommendation systems

Collaborative filtering recommender systems using tag information

Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviours such as purchase behaviour, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data