Recommender Systems Research: A Connection-Centric Survey

Perugini, Saverio; Gonçalves, Marcos André; Fox, Edward A.

doi:10.1023/b:jiis.0000039532.05533.99

Cited by 136 publications

(78 citation statements)

References 97 publications

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“…2, there are feature-based, knowledge-based, behavior-based, citation-based, contextbased, ruse-based, and many more recommendation classes [133,187,300,[303][304][305][306]. We consider the following seven classes to be most appropriate for distinguishing the approaches in the field of research-paper recommender systems:…”

Section: Survey Of the Recommendation Classesmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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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 contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden 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 aspect...

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Section: Survey Of the Recommendation Classesmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…Social information is used by researchers with three primary objectives: (a) to improve the quality of predictions and recommendations (Arazy, Kumar, & Shapira, 2009;Carrer-Neto, Hernández-Alcaraz, Valencia-García, & García-Sánchez, 2012), (b) propose or generate new RS (Siersdorfer &Sizov, 2009;Li, Liao, & Lai, 2011;Zhu & Lerman, 2014), and (c) elucidate the most significant relationships between social information and collaborative processes (Perugini, Gonçalves, & Fox, 2004;Hossain & Fazio, 2009). …”

Section: Social Rsmentioning

confidence: 99%

“…RS show a clear trend to allow users to introduce content (Perugini, Gonçalves, & Fox, 2004;Arazy, Kumar, & Shapira, 2009), such as comments, critiques, ratings, opinions and labels as well as to establish social relationship links (e.g., followed, followers, like user and dislike user). This additional information increases the accuracy of predictions and recommendations, which has generated a variety of research articles: Kim, Alkhaldi, Saddik, and Jo (2011), Zheng and Li (2011) and Carrer-Neto, Hernández-Alcaraz, Valencia-García, and García-Sánchez (2012).…”

Section: Social Rsmentioning

confidence: 99%

Incorporating group recommendations to recommender systems: Alternatives and performance

Ortega

Bobadilla

Hernando

et al. 2013

Information Processing & Management

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Proporcionaré una formalización completa del método propuesto. Explicaré cómo obtener el conjunto de k vecinos del grupo de usuarios y mostraré cómo obtener recomendaciones usando dichos vecinos. Asimismo, incluiré un ejemplo detallando cada paso del método propuesto en un sistema de recomendación compuesto por 8 usuarios y 10 items.Las principales características del método propuesto son: (a) es más rápido (más eficiente) que las alternativas proporcionadas por otros autores, y (b) es al menos tan exacto y preciso como otras soluciones estudiadas. Para contrastar esta hipótesis realizaré varios experimentos que miden la precisión, la exactitud y el rendimiento del método. Los resultados obtenidos se compararán con los resultados de otras alternativas utilizadas en la recomendación de grupos. Los experimentos se realizarán con las bases de datos de MovieLens y Netflix. VIII IX ABSTRACTThe importance of recommender systems has grown exponentially with the advent of social networks. In this PhD thesis I will provide a wide vision about the state of the art of recommender systems. They were initially based on demographic, contentbased and collaborative filtering. Currently, these systems incorporate some social information to the recommendation process. In the future, they will use implicit, local and personal information from the Internet of Things.As we will see here, recommender systems based on collaborative filtering can be used to perform recommendations to group of users. Previous works have made this modification in different stages of the collaborative filtering algorithm: establishing the neighborhood, prediction phase and determination of recommended items. In thisPhD thesis I will provide a new method that carry out the unification process (many users to one group) in the first stage of the collaborative filtering algorithm: similarity metric computation.I will provide a full formalization of the proposed method. I will explain how to obtain the k nearest neighbors of the group of users and I will show how to get recommendations using those users. I will also include a running example of a recommender system with 8 users and 10 items detailing all the steps of the method I will present.The main highlights of the proposed method are: (a) it will be faster (more efficient) that the alternatives provided by other authors, and (b) it will be at least as precise and accurate as other studied solutions. To check this hypothesis I will conduct several experiments measuring the accuracy, the precision and the performance of my method. I will compare these results with the results generated by other methods of group recommendation. The experiments will be carried out using MovieLens and Netflix datasets. The kinds of filtering most used at the beginning of the RS (collaborative, content-based and demographic) were described in Pazzani (1999). Breese,

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“…Besides being a critical component of various algorithms, the QR-factorization has a wide range of applications in many disciplines, from datamining to efficient storage and retrieval of high-dimensional data [13], from costumer recommender systems [14] to multiple-input and multiple-output (MIMO) systems such as transmitters and receivers in radio transmissions [15]. In our case of fluid flow analysis, the size of the gathered data (either from large-scale simulations or highly resolved experimental measurements) or the synchronous processing of composite or parameter-dependent data results in snapshot data-matrices with an excessive number of rows (equal to the number of spatial or composite degrees of freedom) but only a rather small number of columns (equal to the number of snapshots).…”

Section: Introductionmentioning

confidence: 99%

Parallel data-driven decomposition algorithm for large-scale datasets: with application to transitional boundary layers

Sayadi

Schmid

2016

Theor. Comput. Fluid Dyn.

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Many fluid flows of engineering interest, though very complex in appearance, can be approximated by low-order models governed by a few modes, able to capture the dominant behavior (dynamics) of the system. This feature has fueled the development of various methodologies aimed at extracting dominant coherent structures from the flow. Some of the more general techniques are based on data-driven decompositions, most of which rely on performing a singular value decomposition (SVD) on a formulated snapshot (data) matrix. The amount of experimentally or numerically generated data expands as more detailed experimental measurements and increased computational resources become readily available. Consequently, the data-matrix to be processed will consist of far more rows than columns, resulting in a so-called tall-and-skinny (TS) matrix. Ultimately, the SVD of such a TS data-matrix can no longer be performed on a single processor and parallel algorithms are necessary. The present study employs the parallel TSQR algorithm of [1], which is further used as a basis of the underlying parallel SVD. This algorithm is shown to scale well on machines with a large number of processors and, therefore, allows the decomposition of very large data-sets. In addition, the simplicity of its implementation and the minimum required communication makes it suitable for integration in existing numerical solvers and data-decomposition techniques. Examples that demonstrate the capabilities of highly parallel datadecomposition algorithms include transitional processes in compressible boundary layers without and with induced flow separation.

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Recommender Systems Research: A Connection-Centric Survey

Cited by 136 publications

References 97 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

Incorporating group recommendations to recommender systems: Alternatives and performance

Parallel data-driven decomposition algorithm for large-scale datasets: with application to transitional boundary layers

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