Adapting Recommendation Diversity to Openness to Experience : A Study of Human Behaviour

Postprin

Prediction, Recommendation and Group Analytics Models in the domain of Mashup Services and Cyber-Argumentation Platform

Mashup application development is becoming a widespread software development practice due to its appeal for a shorter application development period. Application developers usually use web APIs from different sources to create a new streamlined service and provide various features to end-users. This kind of practice saves time, ensures reliability, accuracy, and security in the developed applications. Mashup application developers integrate these available APIs into their applications. Still, they have to go through thousands of available web APIs and chose only a few appropriate ones for their application. Recommending relevant web APIs might help application developers in this situation. However, very low API invocation from mashup applications creates a sparse mashup-web API dataset for the recommendation models to learn about the mashups and their web API invocation pattern. One research aims to analyze these mashup-specific critical issues, look for supplemental information in the mashup domain, and develop web API recommendation models for mashup applications. The developed recommendation model generates useful and accurate web APIs to reduce the impact of low API invocations in mashup application development. Cyber-Argumentation platform also faces a similarly challenging issue. In large-scale cyber argumentation platforms, participants express their opinions, engage with one another, and respond to feedback and criticism from others in discussing important issues online. Argumentation analysis tools capture the collective intelligence of the participants and reveal hidden insights from the underlying discussions. However, such analysis requires that the issues have been thoroughly discussed and participant’s opinions are clearly expressed and understood. Participants typically focus only on a few ideas and leave others unacknowledged and underdiscussed. This generates a limited dataset to work with, resulting in an incomplete analysis of issues in the discussion. One solution to this problem would be to develop an opinion prediction model for cyber-argumentation. This model would predict participant’s opinions on different ideas that they have not explicitly engaged. In cyber-argumentation, individuals interact with each other without any group coordination. However, the implicit group interaction can impact the participating user\u27s opinion, attitude, and discussion outcome. One of the objectives of this research work is to analyze different group analytics in the cyber-argumentation environment. The objective is to design an experiment to inspect whether the critical concepts of the Social Identity Model of Deindividuation Effects (SIDE) are valid in our argumentation platform. This experiment can help us understand whether anonymity and group sense impact user\u27s behavior in our platform. Another section is about developing group interaction models to help us understand different aspects of group interactions in the cyber-argumentation platform. These research works can help develop web API recommendation models tailored for mashup-specific domains and opinion prediction models for the cyber-argumentation specific area. Primarily these models utilize domain-specific knowledge and integrate them with traditional prediction and recommendation approaches. Our work on group analytic can be seen as the initial steps to understand these group interactions

A Network Resource Allocation Recommendation Method with An Improved Similarity Measure

Recommender systems have been acknowledged as efficacious tools for managing information overload. Nevertheless, conventional algorithms adopted in such systems primarily emphasize precise recommendations and, consequently, overlook other vital aspects like the coverage, diversity, and novelty of items. This approach results in less exposure for long-tail items. In this paper, to personalize the recommendations and allocate recommendation resources more purposively, a method named PIM+RA is proposed. This method utilizes a bipartite network that incorporates self-connecting edges and weights. Furthermore, an improved Pearson correlation coefficient is employed for better redistribution. The evaluation of PIM+RA demonstrates a significant enhancement not only in accuracy but also in coverage, diversity, and novelty of the recommendation. It leads to a better balance in recommendation frequency by providing effective exposure to long-tail items, while allowing customized parameters to adjust the recommendation list bias

Two-Sided Value-Based Music Artist Recommendation in Streaming Music Services

Most work on music recommendations has focused on the consumer side not the provider side. We develop a two-sided value-based approach to music artist recommendation for a streaming music scenario. It combines the value yielded for the music industry and consumers in an integrated model. For the industry, the approach aims to increase the conversion rate of potential listeners to adopters, which produces new revenue. For consumers, it aims to improve their utility related to recommendations they receive. We use one year of listening records for 15,000+ Last.fm users to train and test the proposed recommendation model on 143 artists. Compared to collaborative filtering, the results show some improvement in recommendation performance by considering both sides’ value in con-junction with other factors, including time, location, external information and listening behavior