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This study aims to explore whether seeking recommendations for doctors from offline word-of-mouth or online reviews influences patient satisfaction after treatment, and how the source of recommendation affects this effect.

Using a unique dataset of more than three million reviews from a popular Chinese online health community, this study used the coarsened exact matching method and built fixed-effect models to conduct empirical analysis.

The results suggest that selecting doctors according to recommendations can improve patient satisfaction and mitigate their dissatisfaction when encountering service failures. However, online recommendations were found to be less effective than offline sources in improving patient satisfaction.

This study provides important insights into patient satisfaction and doctor-patient relationships by revealing the antecedents of satisfaction and the potential for improving this relationship. It also contributes to the understanding of how recommendations in the healthcare context can improve patient satisfaction and alleviate the negative impact of service failures.

The current study aims to empirically analyze the influence of different information sources, together with the persuasiveness of recommender systems (RSs) on the consumer’s purchase intention (PI). It also expands the research on RSs from the point of view of consumer behavior and psychology, considering perceived usefulness and relevance. In addition, it analyzes how different types of personalized recommendations, along with non-personalized ones, influence PI.

The proposed model has been validated using partial least squares structural equation modeling (PLS-SEM), based on the data collected from 597 online shoppers.

This study proves that both information search and RSs influence PI, being complementary rather than mutually exclusive. Recommender systems’ findings indicate that the PI is primarily influenced by the perceived relevance of RSs, the information provided by manufacturers and reviews. Moreover, only the influence of the perceived usefulness of personalized recommendations strongly affects PI. Conversely, non-personalized recommendations do not affect PI.

Developers should focus on increasing the perceived usefulness and relevance of RSs. Thus, they could adopt the hybridization of RSs with the aggregation of both personal shopping behavior and social network contacts. It should integrate information signals from multiple sources to include sentiment extracted from reviews or links to the manufacturer’s page. Furthermore, the recommendation of discounted products must be only for products preferred by customers, because only these influence the PI.

This research provides a structural model that examines together, for the first time, the influence on the PI of the main RSs and sources of information.

This paper aims to utilize an extended involvement-commitment and trust commitment model to examine post-consumption decisions related to food delivery app use.

A self-administered online survey was used to collect data from food delivery app users in the USA.

Findings validate a favorable role of perceived app security and menu description on trust in app recommendations. Trust was found to be positively related to involvement, commitment and willingness to provide feedback. The positive moderating role of perceived convenience and rewards and incentives was also confirmed in relation to consumers’ trust in app recommendations, and involvement and commitment

A key contribution of this study includes the development of a comprehensive model to understand postconsumption decisions related to the usage of food delivery apps. To the best of the authors’ knowledge, this study is also the first to unveil the antecedent and moderating factors related to food delivery app users’ willingness to provide feedback, share personal data and to pay more.

The paper aims to investigate differences in non-professional and professional stock investors’ trust in and tendency to follow financial analysts’ buy and sell recommendations.

Online experiment conducted in Sweden in March 2022 comparing non-professional private investors (n = 80), professional investors (n = 33), and master students in finance (n = 28). Information was presented about four company stocks listed on the New York stock exchange. Two stocks were buy-recommended and two stocks sell-recommended by financial analysts. For one stock of each type, the recommendation was presented to participants. Dependent variables were predictions of the stock price after three months, ratings of confidence in the predictions and choices of holding, buying or selling the stock. Ratings were also made of the importance of presented stock-related information as well as trust in analysts’ skill and integrity.

More positive return predictions were made of buy-recommended than sell-recommended stocks. Non-professionals and to some degree finance students tended to trust financial analysts more than professional investors did and they were more influenced by the presentation of the buy recommendations. All groups made too optimistic return predictions, but the professionals were less confident in their predictions, more likely to sell the stocks and lost less on their investments.

A new finding is that non-professional stock investors are more likely than professional stock investors to trust financial analysts and follow their recommendations. It suggests that financial analysts’ recommendations influence non-professional investors to take unmotivated investment risks. Non-professionals in the stock market should hence be advised to exercise more caution in following analysts’ recommendations.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems. However, the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests. To solve this problem, this study proposes a new user-item composite filtering (UICF) recommendation framework by leveraging temporal semantics.

The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’ latest interest degrees. For an item to be probably recommended to a user, the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.

Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework. Experimental results show that the UICF outperformed three well-known recommendation algorithms Item-Based Collaborative Filtering (IBCF), User-Based Collaborative Filtering (UBCF) and User-Popularity Composite Filtering (UPCF) in the root mean square error (RMSE), mean absolute error (MAE) and F1 metrics, especially yielding an average decrease of 11.9% in MAE.

A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model. It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively, resulting in more accurate and personalized recommendations.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

The Multi-Stakeholder Recommendation System learns consumer and producer preferences to make fair and balanced recommendations. Exclusive consumer-focused studies have improved the recommendation accuracy but lack in addressing producers' priorities for promoting their diverse items to target consumers, resulting in minimal utility gain for producers. These techniques also neglect latent and implicit stakeholders' preferences across item categories. Hence, this study proposes a personalized diversity-based optimized multi-stakeholder recommendation system by developing the deep learning-based diversity personalization model and establishing the trade-off relationship among stakeholders.

The proposed methodology develops the deep autoencoder-based diversity personalization model to investigate the producers' latent interest in diversity. Next, this work builds the personalized diversity-based objective function by evaluating the diversity distribution of producers' preferences in different item categories. Next, this work builds the multi-stakeholder, multi-objective evolutionary algorithm to establish the accuracy-diversity trade-off among stakeholders.

The experimental and evaluation results over the Movie Lens 100K and 1M datasets demonstrate that the proposed models achieve the minimum average improvement of 40.81 and 32.67% over producers' utility and maximum improvement of 7.74 and 9.75% over the consumers' utility and successfully deliver the trade-off recommendations.

The proposed algorithm for measuring and personalizing producers' diversity-based preferences improves producers' exposure and reach to various users. Additionally, the trade-off recommendation solution generated by the proposed model ensures a balanced enhancement in both consumer and producer utilities.

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.