Hybrid data mining processes are employed in recent days on several applications to achieve a better prediction and classification rate along with customer satisfaction. Hybrid data mining processes are the combination of different form of data considered for a neural network decision. In some cases, the different form of data represents image along with numerical data. In the proposed work, a food recommendation system is developed with respect to the flavour taste of the customer and considering the review comments of previous customers. The suggestions given by the users are taken into account as a feedback layer in the neural network for fine tuning the accuracy of the prediction process. The architectural design of the proposed model is employed with an ADNet (Adaptively Dense Convolutional Neural Network) algorithm to enable the usage of low range features in an efficient way. To verify the performance of the developed model, a pizza flavour recommender dataset is employed in the work for analysis. The experimental work analysis indicates that the ADNet algorithm works in a better way on a hybrid data analysis than the traditional DenseNet and ResNet algorithms.

Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process

Abstract News recommender systems provide a technological architecture that helps shaping public discourse. Following a normative approach to news recommender system design, we test utility and external effects of a diversity-aware news recommender algorithm. In an experimental study using a custom-built news app, we show that diversity-optimized recommendations (1) perform similar to methods optimizing for user preferences regarding user utility, (2) that diverse news recommendations are related to a higher tolerance for opposing views, especially for politically conservative users, and (3) that diverse news recommender systems may nudge users towards preferring news with differing or even opposing views. We conclude that diverse news recommendations can have a depolarizing capacity for democratic societies.

https://www.tandfonline.com/doi/pdf/10.1080/21670811.2021.2021804?needAccess=true

Benefits of Diverse News Recommendations for Democracy: A User Study

Most of the available recommender systems focus on the accuracy of recommendations. As a result, their recommendations are often popular and very close to user preferences, which make them repetitious and predictable, hence adversely affecting user satisfaction. Recent studies on recommender systems, however, aim for factors beyond accuracy as accuracy alone cannot ensure the satisfaction of all users. One of the most important criteria beyond the accuracy is serendipity, which includes relevant, unexpected, and novel recommendations that cannot be easily discovered by users themselves. In this paper, a Convolutional Neural Network (CNN) is integrated with the Particle Swarm Optimization (PSO) algorithm to generate serendipitous recommendations. The proposed method is based on the focus shift points, consisting of unexpectedness and relevance parameters. In this approach, these points are considered as the factors showing whether recommendations are serendipitous. The CNN is employed to predict the focus shift points for each user. Then, the PSO is utilized to search for recommendations close to the predicted focus shift points and generate the list of candidate recommendations. After that, the Serendipitous Personalized Ranking (SPR) method is employed to re-rank the candidate recommendations and generate the final list. According to the evaluation results, the proposed approach outperforms other state-of-the-art methods in SRDP, Hit Ratio, and NDCG factors.

Deep neural network approach for a serendipity-oriented recommendation system

The shift to e-commerce has changed many business areas. Real estate is one of the applications that has been affected by this modern technological wave. Recommender systems are intelligent models that assist users of real estate platforms in finding the best possible properties that fulfill their needs. However, the recommendation task is substantially more challenging in the real estate domain due to the many domain-specific limitations that impair typical recommender systems. For instance, real estate recommender systems usually face the clod-start problem where there are no historical logs for new users or new items, and the recommender system should provide recommendations for these new entities. Therefore, the recommender systems in the real estate market are different and substantially less studied than in other domains. In this article, we aim at providing a comprehensive and systematic literature review on applications of recommender systems in the real estate market. We evaluate a set of research articles (13 journal and 13 conference papers) which represent the majority of research and commercial solutions proposed in the field of real estate recommender systems. These papers have been reviewed and categorized based on their methodological approaches, the main challenges that they addressed, and their evaluation procedures. Based on these categorizations, we outlined some possible directions for future research.

Recommender Systems in the Real Estate Market—A Survey

Configurators can be evaluated in various ways such as efficiency and completeness of solution search, optimality of the proposed solutions, usability of configurator user interfaces, and configuration consistency. Due to the increasing size and complexity of feature models, the integration of recommendation algorithms with feature model configurators becomes relevant. In this paper, we show how the output of a recommender system can be evaluated within the scope of feature model configuration scenarios. Overall, we argue that the discussed ways of measuring recommendation quality help developers to gain a broader view on evaluation techniques in constraint-based recommendation domains.

Evaluating recommender systems in feature model configuration

A recommender system is employed to accurately recommend items, which are expected to attract the user’s attention. The over-emphasis on the accuracy of the recommendations can cause information over-specialization and make recommendations boring and even predictable. Novelty and diversity are two partly useful solutions to these problems. However, novel and diverse recommendations cannot merely ensure that users are attracted since such recommendations may not be relevant to the user’s interests. Hence, it is necessary to consider other criteria, such as unexpectedness and relevance. Serendipity is a criterion for making appealing and useful recommendations. The usefulness of serendipitous recommendations is the main superiority of this criterion over novelty and diversity. The bulk of studies of recommender systems have focused on serendipity in recent years. Thus, a systematic literature review is conducted in this paper on previous studies of serendipity-oriented recommender systems. Accordingly, this paper focuses on the contextual convergence of serendipity definitions, datasets, serendipitous recommendation methods, and their evaluation techniques. Finally, the trends and existing potentials of the serendipity-oriented recommender systems are discussed for future studies. The results of the systematic literature review present that the quality and the quantity of articles in the serendipity-oriented recommender systems are progressing.

Reza Jafari Ziarani

Papers

Serendipity in Recommender Systems: A Systematic Literature Review

Deep neural network approach for a serendipity-oriented recommendation system