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Web site recommendation systems help to get high quality information. The modelling of recommendation systems involves the combination of many features: metrics of quality, quality criteria, recommendation criteria, user profile, and specific domain concepts, among others. At the moment of the specification of a recommendation system it must be guaranteed a right interrelation of all of these features. The purpose of this paper is to model a web site quality‐based recommendation system by an ontology network.

In this paper, the authors propose an ontology network based process for web site recommendation modelling. The ontology network conceptualizes the different domains (web site domain, quality assurance domain, user context domain, recommendation criteria domain, specific domain) in a set of interrelated ontologies. Particularly, this approach is illustrated for the health domain.

Basically, this work introduces the semantic relationships that were used to construct this ontology network. Moreover, it shows the usefulness of this ontology network for the detection of possible inconsistencies when specifying recommendation criteria.

Recommendation systems based on ontologies that model the user profile and the domain of resources to be recommended are quite common. However, it is uncommon to find models that explicitly represent the criteria used by the recommender systems, that express the quality dimensions of resources and on which criteria are applied, and consider the user context at the moment of the query.

In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.

A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.

A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.

Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.

Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes.

The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector.

This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions.

To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。

我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。

我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。

据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

Recommender agents are used to make recommendations of interesting items in a wide variety of application domains, such as web page recommendation, music, e‐commerce, movie recommendation, tourism, restaurant recommendation, among others. Despite the various and different domains in which recommender agents are used and the variety of approaches they use to represent user interests and make recommendations, there is some functionality that is common to all of them, such as user model management and recommendation of interesting items. This paper aims at generalizing these common behaviors into a framework that enables developers to reuse recommender agents' main characteristics in their own developments.

This work presents a framework for recommendation that provides the control structures, the data structures and a set of algorithms and metrics for different recommendation methods. The proposed framework acts as the base design for recommender agents or applications that want to add the already modeled and implemented capabilities to their own functionality. In contrast with other proposals, this framework is designed to enable the integration of diverse user models, such as demographic, content‐based and item‐based. In addition to the different implementations provided for these components, new algorithms and user model representations can be easily added to the proposed approach. Thus, personal agents originally designed to assist a single user can reuse the behavior implemented in the framework to expand their recommendation strategies.

The paper describes three different recommender agents built by materializing the proposed framework: a movie recommender agent, a tourism recommender agent, and a web page recommender agent. Each agent uses a different recommendation approach. PersonalSearcher, an agent originally designed to suggest interesting web pages to a user, was extended to collaboratively assist a group of users using content‐based algorithms. MovieRecommender recommends interesting movies using an item‐based approach and Traveller suggests holiday packages using demographic user models. Findings encountered during the development of these agents and their empirical evaluation are described here.

The advantages of the proposed framework are twofold. On the one hand, the functionality provided by the framework enables the development of recommender agents without the need for implementing its whole set of capabilities from scratch. The main processes and data structures of recommender agents are already implemented. On the other hand, already existing agents can be enhanced by incorporating the functionality provided by the recommendation framework in order to act collaboratively.

Tourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.

The authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.

The authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.

This study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

This 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.

It 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.

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 hybridized system outperforms over other existing recommender system.

The 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.

This paper aims to make it convenient for those who have only just begun their research into Community Question Answering (CQA) expert recommendation, and for those who are already concerned with this issue, to ease the extension of our understanding with future research.

In this paper, keywords such as “CQA”, “Social Question Answering”, “expert recommendation”, “question routing” and “expert finding” are used to search major digital libraries. The final sample includes a list of 83 relevant articles authored in academia as well as industry that have been published from January 1, 2008 to March 1, 2019.

This study proposes a comprehensive framework to categorize extant studies into three broad areas of CQA expert recommendation research: understanding profile modeling, recommendation approaches and recommendation system impacts.

This paper focuses on discussing and sorting out the key research issues from these three research genres. Finally, it was found that conflicting and contradictory research results and research gaps in the existing research, and then put forward the urgent research topics.

Because of the extensive user coverage of news sites and apps, greater social and commercial value can be realized if users can access their favourite news as easily as possible. However, news has a timeliness factor; there are serious cold start and data sparsity in news recommendation, and news users are more susceptible to recent topical news. Therefore, this study aims to propose a personalized news recommendation approach based on topic model and restricted Boltzmann machine (RBM).

Firstly, the model extracts the news topic information based on the LDA2vec topic model. Then, the implicit behaviour data are analysed and converted into explicit rating data according to the rules. The highest weight is assigned to recent hot news stories. Finally, the topic information and the rating data are regarded as the conditional layer and visual layer of the conditional RBM (CRBM) model, respectively, to implement news recommendations.

The experimental results show that using LDA2vec-based news topic as a conditional layer in the CRBM model provides a higher prediction rating and improves the effectiveness of news recommendations.

This study proposes a personalized news recommendation approach based on an improved CRBM. Topic model is applied to news topic extraction and used as the conditional layer of the CRBM. It not only alleviates the sparseness of rating data to improve the efficient in CRBM but also considers that readers are more susceptible to popular or trending news.

Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation.

First, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-system

Experimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system.

This paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.