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Cold Start problems in recommender systems pose various challenges in the adoption and use of recommender systems, especially for new item uptake and new user engagement. This restricts organizations to realize the business value of recommender systems as they have to incur marketing and operations costs to engage new users and promote new items. Owing to this, several studies have been done by recommender systems researchers to address the cold start problems. However, there has been very limited recent research done on collating these approaches and algorithms. To address this gap, the paper conducts a systematic literature review of various strategies and approaches proposed by researchers in the last decade, from January 2010 to December 2021, and synthesizes the same into two categories: data-driven strategies and approach-driven strategies. Furthermore, the approach-driven strategies are categorized into five main clusters based on deep learning, matrix factorization, hybrid approaches, or other novel approaches in collaborative filtering and content-based algorithms. The scope of this study is limited to a systematic literature review and it does not include an experimental study to benchmark and recommend the best approaches and their context of use in cold start scenarios.

Recommender systems are tools that help users in the decision-making process of choosing items that may be relevant for them among a vast amount of other items. One of the main problems of recommender systems is the cold start problem, which occurs when either new items or new users are added to the system and, therefore, there is no previous information about them. This article presents a multi-source dataset optimized for the study and the alleviation of the cold start problem. This dataset contains info about the users, the items (movies), and ratings with some contextual information. The article also presents an example user behavior-driven algorithm using the introduced dataset for creating recommendations under the cold start situation. In order to create these recommendations, a mixed method using collaborative filtering and user-item classification has been proposed. The results show recommendations with high accuracy and prove the dataset to be a very good asset for future research in the field of recommender systems in general and with the cold start problem in particular.

In the recommendation system (RS) literature, a distinction exists between studies dedicated to fully operational (known users/items) and cold-start (new users/items) RSs. The warm-up phase—the transition between the two—is not widely researched, despite evidence that attrition rates are the highest for users and content providers during such periods. RS formulations, particularly deep learning models, do not easily allow for a warm-up phase. Herein, we propose two independent and complementary models to increase RS performance during the warm-up phase. The models apply to any cold-start RS expressible as a function of all user features, item features, and existing users’ preferences for existing items. We demonstrate substantial improvements: Accuracy-oriented metrics improved by up to 14% compared with not handling warm-up explicitly. Non-accuracy-oriented metrics, including serendipity and fairness, improved by up to 12% compared with not handling warm-up explicitly. The improvements were independent of the cold-start RS algorithm. Additionally, this paper introduces a method of examining the performance metrics of an RS during the warm-up phase as a function of the number of user–item interactions. We discuss problems such as data leakage and temporal consistencies of training/testing—often neglected during the offline evaluation of RSs.

With the advance of sentiment analysis techniques, several studies have been on Multi-Criteria Recommender Systems (MCRS) leveraging sentiment information. However, partial preferences quite and naturally happen in MCRS and negatively affect the predictive performances of sentiment analysis and multi-criteria recommendation. In this paper, we propose a Sentiment Aware Tensor Model-based MCRS named SATM. It maps between i) a set of multiple classes from explicit user feedbacks and ii) sentiments extracted from free texts in user reviews. In particular, we found the four patterns of the partial preferences and applied a rule-based function to detect them and fill their incomplete ratings intuitively. Lastly, we introduce a mapping function of the misinterpretable patterns into sentiment scores in order to generate virtual user preferences that construct the SATM. Experiments on three datasets (i.e., hotel and restaurant reviews) collected from TripAdvisor show that the SATM is superior to various baseline techniques, including state-of-the-art approaches. Additionally, the experimental evaluation of the SATM’s variants reveals that the rule-based and mapping functions can handle the partial preferences and improve the MCRS’ performance, regardless of target domains.

A Recommender System (RS) works much better for users when it has more information. In Collaborative Filtering, where users' preferences are expressed as ratings, the more ratings elicited, the more accurate the recommendations. New users present a big challenge for a RS, which has to providing content fitting their preferences. Generally speaking, such problems are tackled by applying Active Learning (AL) strategies that consist on a brief interview with the new user, during which she is asked to give feedback about a set selected items. This article presents a comprehensive study of the most important techniques used to handle this issue focusing on AL techniques. The authors then propose a novel item selection approach, based on Multi-Criteria ratings and a method of computing weights of criteria inspired by a multi-criteria decision making approach. This selection method is deployed to learn new users' profiles, to identify the reasons behind which items are deemed to be relevant compared to the rest items in the dataset.

Research on the problem of new user cold-start recommendation generally leverages user side information to suggest items to new users. This approach, however, is impractical due to privacy concerns. In this paper, we propose ColdGAN, an end-to-end GAN-based recommendation system that makes no use of side information to resolve the new user cold-start recommendation problem. The proposed ColdGAN explores the merit of GAN that enables precise data generation given imprecise data. Our generative network learns to predict item ratings that cold-start users would make in the future given their limited rating behavior data. The predicted ratings are evaluated by the discriminative network trained for determining whether the ratings are precise enough. Moreover, a novel rejuvenation function and relevant item loss are incorporated into ColdGAN to enhance the predictions made by the learned generative network. Experiments based on three real-world datasets demonstrate that ColdGAN significantly outperforms many state-of-the-art recommendation systems. Also, our designed rejuvenation function and relevant item loss are effective in guiding our generative network to infer item ratings of cold-start new users.

Collaborative filtering systems typically need to acquire some data about the new user in order to start making personalized suggestions, a situation commonly referred to as the “new user problem”. In this work we attempt to address the new user problem via a unique personalized strategy for prompting the user with articles to rate. Our approach makes use of hypernyms extracted from the WordNet database and proves to be converging fast to the actual user interests based on minimal user ratings, which are provided during the registration process. In addition, we explore the possible enhancement of the document clustering results, and in particular clustering of news articles from the web, when using word-based n-grams during the keyword extraction phase. We present and evaluate a weighting approach that combines clustering of news articles derived from the web, using n-grams that are extracted from the articles at an offline stage. This technique is then compared with the single minded “bag-of-words” representation that our clustering algorithm, W-kmeans, previously used. Our experimentation reveals that via fine tuning the weighting parameters between keyword and n-grams, as well as the n value itself, a significant improvement regarding the clustering results metrics can be achieved.

This chapter contains sections titled: Introduction Background and Related Work Cooperative Communications Relay‐Assisted Communications Two‐Way Relay‐Assisted Communications Relay‐Assisted Communications With Reuse of Resources Appendix 11.1 References

PurposeResearch on the Internet of Things (IoT) has gained momentum in various industry contexts. However, the literature lacks broad empirical evidence on the factors that influence users' intention to adopt this cutting-edge technology, especially in the food and beverage industry (F&BI) – a significant yet unexplored setting. Therefore, the authors aim to extend the “Unified Theory of Acceptance and Use of Technology (UTAUT)” model by coupling it with perceived collaborative advantage, organizational inertia and perceived cost and explore the key determinants of IoT adoption for the digital transformation of the F&BI.Design/methodology/approachThis study employs a cross-sectional quantitative approach, where a sample of 307 usable responses was drawn from the senior managers of the Australian F&BI.FindingsThe authors have found that performance expectancy, perceived collaborative advantage, effort expectancy, social influence and facilitating conditions have a strong positive influence on the behavioural intention to adopt IoT for the digital transformation of the F&BI. Furthermore, while high perceived costs and organizational inertia are often considered negative factors in adopting new technology, our results reveal the insignificant influence of these factors on the adoption of IoT, which is interesting. The findings also suggest that age and voluntariness significantly moderate most of the relationships, while gender is an insignificant moderator.Originality/valueThe study provides several novel insights into the existing body of knowledge by extending the UTAUT model with three variables and applying it in a unique context.

Why do Internet, financial service, and beer commercials dominate Super Bowl advertising? How do political ceremonies establish authority? Why does repetition characterize anthems and ritual speech? Why were circular forms favored for public festivals during the French Revolution? This book answers these questions using a single concept: common knowledge. Game theory shows that in order to coordinate its actions, a group of people must form \"common knowledge.\" Each person wants to participate only if others also participate. Members must have knowledge of each other, knowledge of that knowledge, knowledge of the knowledge of that knowledge, and so on. Michael Chwe applies this insight, with striking erudition, to analyze a range of rituals across history and cultures. He shows that public ceremonies are powerful not simply because they transmit meaning from a central source to each audience member but because they let audience members know what other members know. For instance, people watching the Super Bowl know that many others are seeing precisely what they see and that those people know in turn that many others are also watching. This creates common knowledge, and advertisers selling products that depend on consensus are willing to pay large sums to gain access to it. Remarkably, a great variety of rituals and ceremonies, such as formal inaugurations, work in much the same way. By using a rational-choice argument to explain diverse cultural practices, Chwe argues for a close reciprocal relationship between the perspectives of rationality and culture. He illustrates how game theory can be applied to an unexpectedly broad spectrum of problems, while showing in an admirably clear way what game theory might hold for scholars in the social sciences and humanities who are not yet acquainted with it. In a new afterword, Chwe delves into new applications of common knowledge, both in the real world and in experiments, and considers how generating common knowledge has become easier in the digital age.