Data visualization recommendation: Literature review and future perspectives

The rapid growth of web information provides excellent opportunities for developing e-services in many applications but also caused increasingly severe information overload problems whereby users are not able to locate relevant information to exactly meet their needs efficiently by using the current Internet search functions. A personalised recommender system aims to handle this issue. A big challenge in current recommender system research is: the items and user profiles in many recommender system applications nowadays, such as the e-business and e-learning recommender systems, are so complex that they can only be described in complicated tree structures. Therefore, the item or user similarity measure, as the core technique of the recommendation approach, becomes a tree similarity measure, which existing recommender systems cannot provide. Another challenge is that in many real life situations, online recommendations to customers in selecting the most suitable products/services are often made under incomplete and uncertain information, which needs fuzzy set theory and techniques to deal with. Thus, how to use fuzzy set techniques to handle data uncertainty issues in tree-structured items or user profiles needs to be investigated. This research aims to handle these two challenges in both theoretical and practical aspects. It first defines a tree-structured data model, which can be used to model tree-structured items, user profiles and user preferences. A comprehensive similarity measure on tree-structured data considering all the information on tree structures, nodes’ concepts, weights and values is then developed, which can be used to compute the semantic similarity between tree-structured items or users, and the matching degree of items to tree-structured user requests. Based on the tree-structured data model, the tree-structured items and user requirements are modelled as item trees and user request trees respectively. An item tree and user request tree-based hybrid recommendation approach is then developed. To model users’ fuzzy tree-structured preferences, a fuzzy preference tree model is proposed. A fuzzy preference tree-based recommendation approach is then developed. Experimental results on an Australian business dataset and the Movielens dataset show that the proposed recommendation approaches have good performance and are well-suited in dealing with tree-structured data in recommender systems. By use of the proposed tree similarity measure and recommendation approaches based on that, two real world applications, a business partner recommender system, Smart BizSeeker, and an e-learning recommender system, ELRS, are designed and implemented, which demonstrate the applicability and effectiveness of the proposed approaches.

Consumer psychology and consumer behaviour has constantly been a field of interest for the researchers. Volumes of researches are available in this area, still new theories and concepts keep emerging in response to change in context and environment. Consumer psychology has large cross disciplinary implications. In this paper, a relationship between consumer psychology and recommender system has been explained and how implementing a consumer psychology model can improve recommender system performance. This paper also gives first level analysis of various filters that can be developed along with the design of recommender system in order to generate a more refined and relevant choice sets. There are attraction effects observed among different items and the attractiveness of a product is codependent on attractiveness of other options available in a choice set. In the present paper, we have explored the utility of defender model in the design of recommender systems. Different effects like decoy and asymmetric dominance are also analyzed to achieve better efficiency and effectiveness in the design of recommender system. Keywords: Decoy, Recommender, Defender Model, Asymmetric Dominance INTRODUCTION Recommender systems are the category of information systems which supports searching and suggesting products and services according to user's requirement and preferences (Resnick et al., 1997). The product range may vary from consumer durables such as a book in Amazon or digital goods such as song on lastfm. The task of recommending products proactively is performed by recommender systems which work backstage on E-commerce websites. Many recommendation approaches and methods are applied to target internet based consumers. Collaborative filtering was earliest approach to create user to user similarity and then recommend similar products. Recommendation approach was simple. Positively rated items by one set of user were recommended to similar users. However, this 'user to user' correlation lead to scarce data and cold start problems (Ahn, 2008). This problem was targeted by designing an 'item to item' based correlation and this category of algorithms were termed as content based filtering as volume of data available for products was much denser than user data. There were some specialized variations also for example demographic or knowledge based recommender system which includes domain based knowledge to analyze user preference and predict user choices. As it is evident that recommender system directly interact with consumer and for establishing a better communication it is essential that recommender systems should be intelligent enough to understand consumer's behavioral traits(Pu et al., 2011). Understanding consumer psychology and its phenomenon will actually present meaningful insights to improve recommendations and effectiveness of these systems which in turn can be translated into economical benefits. The purpose of the recommender system is not only to elicit preference and produce accurate search results but also to improve the decision making in an ecommerce context (Xiao et al., 2007). A good amount of research in the recommender system focuses on developing complex algorithms and software programs which improves the efficiency of recommender systems (Cosley et al., 2003). However, there is a need to identify other significant aspects which attempts to maximize the value derived from a recommender system experience (Xiao et al., 2007). Factors such as trust on online medium (Yaobin et al., 2007) and personal choices (Ho et al., 2008) are often ignored in the design and implementation process. .In the present paper, we propose a conceptual framework based on established theories from the field of consumer psychology. The proposed framework allows the systems designer to incorporate the significance of derived value of a given product/service in order to achieve the single most important objective of any decision behaviour-maximizing utility. …

PurposeThis paper aims to describe the usage of a hybrid weightage-based recommender system focused on books and implementing it at an industrial level, using various recommendation approaches. Additionally, it focuses on integrating the model into the most widely used platform application.Design/methodology/approachIt is an industrial level implementation of a recommendation system by applying different recommendation approaches. This study describes the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application.FindingsThis paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the hybridized system outperforms over other existing recommender system.Originality/valueThe proposed recommendation system is an industrial level implementation of a recommendation system by applying different recommendation approaches. The recommendation system is centralized to books and its recommendation. In this paper, the authors also describe the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application. This paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the newly created hybridized system outperforms the Netflix recommendation model as well as the Hybrid book recommendation system model as has been clearly shown in the Results Analysis section of the book. The source-code can be available at https://github.com/debajyoty/recomender-system.git.

Systematic Literature Reviews (SLRs) play a vital role in the software engineering field by synthesizing existing knowledge, identifying research gaps, and guiding future investigations with methodological rigor. Given the rapid growth of published research, techniques such as snowballing are essential. Forward snowballing, in particular, helps to discover newer studies that cite key seed papers, enhancing the completeness of SLRs. Despite its value, when performed manually, it remains a labor-intensive and error-prone task. To address this challenge, we mapped existing tools that support forward snowballing. Our analysis focuses on the capabilities of these tools to automate core tasks, including the identification of relevant articles and the extraction of bibliographic metadata. We critically examine their reliability by comparing their outputs against a manually curated forward snowballing process. Key evaluation criteria include the completeness and relevance of the metadata retrieved, the quality of the retrieved scientific databases, and the ease of integration into SLR workflows. As future research, based on our findings, we will propose a framework that takes advantage of existing tools to automate forward snowballing.

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid?1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors “Recommender system”, “Recommendation system”, “Personalization system”, “Collaborative filtering” and “Contents filtering”. The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master’s and doctoral dissertations, textbook, unpublished working papers, non?English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k?nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

Recommender systems were first conceived to provide suggestions of interesting items to users. The evolution of such systems provided an understanding that a recommender system is currently used to diverse objectives. One of the current challenges in the field is to have approaches of recommendation that go beyond accuracy metrics. Since it is a very recent interest of the community, this review, also characterized as an exploratory search, provides an overview of the techniques in the area that tries to look beyond accuracy. More specifically, one of the characteristics that would provide such evolution to these systems is the adaptation. This review is then performed to find the existence and characteristics of such approaches. Of the total 438 papers returned in the submission of the search string, 57 papers were analyzed after two filtering processes. The papers have shown that the area is little explored and one of the reasons is the challenge to validate non-accuracy characteristics in such approaches.

Is diversity optimization always suitable? Toward a better understanding of diversity within recommendation approaches

The past decade has seen a fast rise in popularity of recommendation systems provided by many entertainment and social media services. However, despite the recognised advances in different recommendation approaches and technologies, there remain many challenges, particularly in TV content recommendation systems. More precisely, machine learning based TV content recommendation systems suffer from a class imbalance problem; hence, it is difficult to evaluate the system using traditional metrics. Moreover, specific challenges arise during the development phase, when the system operates in ‘offline’ mode. This means the recommendations are not actually presented to users - making it even more difficult to measure the quality of those recommendations. This paper presents a proof-of-concept demonstrator of a television recommendation system, based on Content-based Filtering, as a contribution towards building a full-scale intelligent recommendation system. New evaluation metrics are proposed for ‘offline’ testing mode, while also tackling the class imbalance problem. The experimental results, based on real usage data, are promising and help in defining the future path as presented along with the conclusion.

With the advancement of the Internet, technology, and communication channels, the generation of tourist data has significantly increased across various sectors such as hotels, restaurants, transportation, heritage sites, tourist events, and activities. This surge is particularly notable with the rise of Online Travel Agencies (OTAs). However, the sheer volume of options provided to tourists by web search engines or specialized tourism websites often overwhelms them, burying relevant results in a sea of information "noise." This inundation hinders or slows down the decision-making process. To alleviate this issue and aid tourists in trip planning by facilitating the discovery of relevant information, numerous recommender systems have emerged. This article offers an overview of the diverse recommendation approaches employed in the tourism domain. Through this investigation, we propose an architecture and conceptual framework for a tourism recommender system founded on a hybrid recommendation approach. This system surpasses merely suggesting a list of tourist attractions tailored to individual preferences. Instead, it functions as a comprehensive trip planner, crafting detailed itineraries comprising a variety of tourism resources, customized for specific visit durations. KeyWords: Internet Development, Tourist Data Production, Trip Planning, Conceptual Framework.

Acid soils cover about 30% of the total ice-free land area. Based on this, the study aimed to evaluate soil acidity correction, focusing on identifying studies on limestone recommendations and corrective measures in agricultural systems. To do so, a systematic literature review (SLR) was carried out, composed of: 1) a systematic literature mapping (SLM), which identifies studies using topic-specific keywords and database automatic search; 2) Snowballing, technique that expands the initial selection using references of the selected studies; and 3) a SLR to synthesize the selected papers. First, 169 scientific papers were selected, of which 116 by SLM, 26 by backward Snowballing, and 27 by forward Snowballing. Most of the papers were about lime requirement (LR) methods (43%) and liming effects on soil and plants (19%). A total of 26 LR methods were found in the papers, of which most were base saturation and Shoemaker-McLean-Pratt methods. Calcium and magnesium carbonates, as well as dolomite, were the main sources of soil acidity correction. In brief, the study provided a critical and constructive analysis of the existing literature on soil acidity correction; the findings showed that liming is the most used and effective method to neutralize soil acidity.

The Web creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems aim to automatically generate personalized suggestions of products/services to customers (businesses or individuals). Although recommender systems have been well studied, there are still two challenges in the development of a recommender system, particularly in real-world B2B e-services: (1) items or user profiles often present complicated tree structures in business applications, which cannot be handled by normal item similarity measures and (2) online users' preferences are often vague and fuzzy, and cannot be dealt with by existing recommendation methods. To handle both these challenges, this study first proposes a method for modeling fuzzy tree-structured user preferences, in which fuzzy set techniques are used to express user preferences. A recommendation approach to recommending tree-structured items is then developed. The key technique in this study is a comprehensive tree matching method, which can match two tree-structured data and identify their corresponding parts by considering all the information on tree structures, node attributes, and weights. Importantly, the proposed fuzzy preference tree-based recommendation approach is tested and validated using an Australian business dataset and the MovieLens dataset. Experimental results show that the proposed fuzzy tree-structured user preference profile reflects user preferences effectively and the recommendation approach demonstrates excellent performance for tree-structured items, especially in e-business applications. This study also applies the proposed recommendation approach to the development of a Web-based business partner recommender system.

Recommender Systems provide users with content or products they are interested in. The main purpose of Recommender Systems is to find, among the vast amount of information that is available or advertised on the Internet, content that meets the user’s needs i.e., a product or content that satisfies his or her wishes. These systems are being used more and more in many of the services of our daily lives. In this paper, a systematic mapping review that explores the use of Rec- ommender Systems in formal learning stages is presented. The paper analyzes what kinds of items the Recommender Systems suggest, who the users that receive the recommendations are, what kinds of information the Recommender Systems use to carry out the recommendation process, the algorithms and techniques the Recommender Systems employ and, finally, how the Recommender Systems have been evaluated. The results obtained in the review will make it possible to iden- tify not only the current situation in this field but also some of the challenges that are still to be faced.

Nowadays, E-commerce websites have been developed and are very popular among online users. Users deal with the problems to choose the right items that meet with their needs. Recommender systems try to suggest the right items to the user by applying different recommendation approaches. Collaborative Filtering recommendation (CF) approach makes recommendations to users using the user-item matrix which has the ratings on each item given by users. Data sparsity problems may occur when a user-item matrix is built based on the ratings of users (one to five stars). User reviews on the products contain more information and opinions than user ratings. This paper proposes the rating prediction approach that infers the user preferences from textual reviews of hotels by performing sentiment analysis. The preference scores obtained from the sentiment analysis are integrated into the rating prediction process which applies two approaches named memory-based CF and model-based CF. The performance of the proposed system is tested on the Myanmar hotel reviews which are crawled from TripAdvisor site and hotel reviews which are downloaded from the Kaggle site. The resulted rating prediction accuracy of two approaches on two data sets is compared by using Root Mean Square Error (RMSE).

The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking “one‐to‐one” e‐services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e‐services. Recommender systems is an effective approach for the implementation of Personalized E‐Service which has gained wide exposure in e‐commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item‐based fuzzy semantic similarity and item‐based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF‐based recommendation approaches, namely sparsity and new “cold start” item problems.

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.