In the last 16 years, more than 200 research articles were published about
research-paper recommender systems
. We reviewed these articles and present some descriptive statistics in this paper, as ...well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users’ information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.
On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information ...overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems.
The project management in the area of Information Technologies (IT) has not proven to yield the results expected, as the failure rates have been documented as higher than success. One explanation for ...such results comes from the huge variants of what is called an IT project, so that there is no single method or “silver bullet” that can serve as an effective tool in all situations. Recognizing the complexity of IT projects and the particularities of organizations implementing IT, the present article revises the literature in ITPM complexity, and proposes a customizing method to choose tools and techniques for IT project management. The article's main purpose is the introduction of a construction logic of a method that collects data for the application of a content-based filtering technique to produce recommendations of bundled tools, based on project characteristics and organizational contexts.
•A hybrid recommender system using an expert system is proposed.•The system combines a CF system, a CBF system and an expert system.•The expert system evaluates the importance of each recommended ...movie.•The system is compared with other typical recommender approaches.•The system is validated on a group of users with promising results.
Currently, the Internet contains a large amount of information, which must then be filtered to determine suitability for certain users. Recommender systems are a very suitable tool for this purpose. In this paper, we propose a monolithic hybrid recommender system called Predictory, which combines a recommender module composed of a collaborative filtering system (using the SVD algorithm), a content-based system, and a fuzzy expert system. The proposed system serves to recommend suitable movies. The system works with favorite and unpopular genres of the user, while the final list of recommended movies is determined using a fuzzy expert system, which evaluates the importance of the movies. The expert system works with several parameters – average movie rating, number of ratings, and the level of similarity between already rated movies. Therefore, our system achieves better results than traditional approaches, such as collaborative filtering systems, content-based systems, and weighted hybrid systems. The system verification based on standard metrics (precision, recall, F1-measure) achieves results over 80%. The main contribution is the creation of a complex hybrid system in the area of movie recommendation, which has been verified on a group of users using the MovieLens dataset and compared with other traditional recommender systems.
•We propose a content-based filtering algorithm based on a multiattribute network.•Network analysis can consider similarities among indirectly-connected items.•The proposed method addresses the data ...sparsity and over-specialization problems.•The experiment with “MovieLens” demonstrates the robustness of the proposed method.
Content-based filtering (CBF), one of the most successful recommendation techniques, is based on correlations between contents. CBF uses item information, represented as attributes, to calculate the similarities between items. In this study, we propose a novel CBF method that uses a multiattribute network to effectively reflect several attributes when calculating correlations to recommend items to users. In the network analysis, we measure the similarities between directly and indirectly linked items. Moreover, our proposed method employs centrality and clustering techniques to consider the mutual relationships among items, as well as determine the structural patterns of these interactions. This mechanism ensures that a variety of items are recommended to the user, which improves the performance. We compared the proposed approach with existing approaches using MovieLens data, and found that our approach outperformed existing methods in terms of accuracy and robustness. Our proposed method can address the sparsity problem and over-specialization problem that frequently affect recommender systems. Furthermore, the proposed method depends only on ratings data obtained from a user's own past information, and so it is not affected by the cold start problem.
In this paper, the problem of user interface recommendations for workflow management systems is investigated. The user interface is automatically adapted using a software tool based on content-based ...filtering. This tool collects information about the way processes are carried out in an organization, analyzes and processes the data, and recommends the next action to the user in order to increase efficiency, facilitate training, and improve decision-making. The proposed tool was verified in the real environment within three organizations. For each organization, after at least several weeks of learning, the tool was able to offer suggestions that were selected by real users.
The use of Online Social Networks has been rapidly increased over the last years. In particular, Social Media Networks allow people to communicate, share, comment and observe different types of ...multimedia content. This phenomenon produces a huge amount of data showing Big Data features, mainly due to their high change rate, large volume and intrinsic heterogeneity. In this perspective, in the last decade Recommender Systems have been introduced to support the browsing of such data collections, assisting users to find “what they really need” within this ocean of information. In this research work, we propose and describe a novel recommending system for big data applications able to provide recommendations on the base of the interactions among users and the generated multimedia contents in one or more social media networks. The proposed system relies on a “user-centered” approach. An experimental campaign, using data coming from many social media networks, has been performed in order to assess the proposed approach also showing how it can obtain very promising results.
•We propose a novel recommending system for big data applications.•We design an user-centered recommendation approach for online social networks.•Modern social networking applications can take the more important advantages of the proposed framework.•Experimental results encourage further research in this direction.
Recommender systems are used to suggest items to users based on their interests. They have been used widely in various domains, including online stores, web advertisements, and social networks. As ...part of their process, recommender systems use a set of similarity measurements that would assist in finding interesting items. Although many similarity measurements have been proposed in the literature, they have not concentrated on actual user interests. This paper proposes a new efficient hybrid similarity measure for recommender systems based on user interests. This similarity measure is a combination of two novel base similarity measurements: the user interest–user interest similarity measure and the user interest–item similarity measure. This hybrid similarity measure improves the existing work in three aspects. First, it improves the current recommender systems by using actual user interests. Second, it provides a comprehensive evaluation of an efficient solution to the cold start problem. Third, this similarity measure works well even when no corated items exist between two users. Our experiments show that our proposed similarity measure is efficient in terms of accuracy, execution time, and applicability. Specifically, our proposed similarity measure achieves a mean absolute error (MAE) as low as 0.42, with 64% applicability and an execution time as low as 0.03 s, whereas the existing similarity measures from the literature achieve an MAE of 0.88 at their best; these results demonstrate the superiority of our proposed similarity measure in terms of accuracy, as well as having a high applicability percentage and a very short execution time.
Recommendation systems are information filtering tools that present items to users based on their preferences and behavior, for example, suggestions about scientific papers or music a user might ...like. Based on what we said and with the development of computer science that has started to take an interest in big data and how it is used to discover user interest, we have found a lot of research going on in the area of recommendation and there are powerful systems available. In the unsupervised learning domain, this paper introduces a novel method for creating a hybrid recommender framework that combines Collaborative Filtering with Content Based Approach and Self-Organizing Map neural network technique. By testing our system on a subset of the Movies Database, we demonstrate that our method outperforms state-of-the-art methods in terms of accuracy and precision, as well as improving the efficiency of the traditional Collaborative Filtering methodology.
•A hybrid recommender system using an expert system is proposed.•The system combines a CF system, a CBF system and an expert system.•The system is compared with other typical recommender ...approaches.•The system is compared with other real online e-shop.•The system is validated and verified on a group of users with promising results.
Nowadays, various recommender systems are popular and their main aim is to recommend suitable content to the user based on various parameters. This article proposes a hybrid recommender system, Eshop recommender, which combines a recommender module composed of three subsystems (the subsystems use collaborative-filtering and content-based approaches) and a fuzzy expert system. It is an e-shopping recommender system for suggesting suitable products. The system works with different user preferences and their activity on the e-shop, and the resulting list of recommended products is created using the fuzzy expert system. The expert system works with several parameters - similarity level with already rated products, coefficient of purchased product, and an average rating of the product. Due to this, our proposed system achieves promising results based on standard metrics (Precision, Recall, F1-measure). The system achieves results above 90%. The system also achieves better results than traditional approaches. The main contribution is creating a comprehensive hybrid system in the area of product recommendation in an online store, which has been validated on a group of real users and compared with other traditional approaches and the recommendation module of another online store.
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