Performance Of Recommendation Algorithms Research Articles

First Things First? Order Effects in Online Product Recommender Systems

Research on recommender systems has noted that the ranking of recommended items may play an important role in the performance of recommendation algorithms. To advance recommender systems research beyond the traditional approach that ranks recommended products in descending, it is crucial to understand the cognitive processes that online consumers experience when they evaluate products in a sequence. Drawing on evaluability theory and the order effects perspective, we formulate a scenario in which two products are presented sequentially and each product has two attributes, one of which can be evaluated independently while the other is difficult to evaluate without comparison. Analyses show that in two out of the three cases examined, presenting the most recommended product in the second place will result in stronger consumer purchase intentions and willingness to pay. Research hypotheses are proposed based on the results of the scenario analyses and are empirically tested through three laboratory experiments. In Study 1, evidence for the hypothesized order effects is found for the settings with randomly assigned product recommendations. In Study 2, the same effects are observed for the settings with personalized recommendations generated by a collaborative filtering algorithm. In Study 3, it is shown that such order effects also exist in terms of the recommendation strength of recommender systems. These findings provide novel insights into the behavioral implications of using recommender systems in e-commerce, shedding light on additional means of improving the design of such systems.

A qualitative study of large-scale recommendation algorithms for biomedical knowledge bases

The frequency at which new research documents are being published causes challenges for researchers who increasingly need access to relevant documents in order to conduct their research. Searching across a variety of databases and browsing millions of documents to find semantically relevant material is a time-consuming task. Recently, there has been a focus on recommendation algorithms that suggest relevant documents based on the current interests of the researchers. In this paper, we describe the implementation of seven commonly used algorithms and three aggregation algorithms. We evaluate the recommendation algorithms in a large-scale biomedical knowledge base with the goal of identifying relative weaknesses and strengths of each algorithm. We analyze the recommendations from each algorithm based on assessments of output as evaluated by 14 biomedical researchers. The results of our research provide unique insights into the performance of recommendation algorithms against the needs of modern-day biomedical researchers.

Research on the Matthews Correlation Coefficients Metrics of Personalized Recommendation Algorithm Evaluation

The personalized recommendation systems could better improve the personalized service for network user and alleviate the problem of information overload in the Internet. As we all know, the key point of being a successful recommendation system is the performance of recommendation algorithm. When scholars put forward some new recommendation algorithms, they claim that the new algorithms have been improved in some respects, better than previous algorithm. So we need some evaluation metrics to evaluate the algorithm performance. Due to the scholar didn’t fully understand the evaluation mechanism of recommendation algorithms. They mainly emphasized some specific evaluation metrics like Accuracy, Diversity. What’s more, the academia did not establish a complete and unified assessment of recommendation algorithms evaluation system which is credibility to do the work of recommendation evaluation. So how to do this work objective and reasonable is still a challengeable task. In this article, we discussed the present evaluation metrics with its respective advantages and disadvantages. Then, we put forward to use the Matthews Correlation Coefficient to evaluate the recommendation algorithm’s performance. All this based on an open source projects called mahout which provides a rich set of components to construct the classic recommendation algorithm. The results of the experiments show that the applicability of Matthews correlation coefficient in the relative evaluation work of recommendation algorithm.

On Efficient Link Recommendation in Social Networks Using Actor-Fact Matrices

Link recommendation is a popular research subject in the field of social network analysis and mining. Often, the main emphasis is put on the development of new recommendation algorithms, semantic enhancements to existing solutions, design of new similarity measures, and so forth. However, relatively little scientific attention has been paid to the impact that various data representation models have on the performance of recommendation algorithms. And by performance we do not mean the time or memory efficiency of algorithms, but the precision and recall of recommender systems. Our recent findings unanimously show that the choice of network representation model has an important and measurable impact on the quality of recommendations. In this paper we argue that the computation quality of link recommendation algorithms depends significantly on the social network representation and we advocate the use of actor-fact matrix as the best alternative. We verify our findings using several state-of-the-art link recommendation algorithms, such as SVD, RSVD, and RRI using both single-relation and multirelation dataset.