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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.

This study aims to investigate how small talk interaction affects knowledge sharing, examining the mediating role of interpersonal trust (affect- and cognition-based trust) and the moderating role of perceived similarity among the mechanisms of small talk and knowledge sharing.

This research conducts complementary studies and collects multi-culture and multi-wave data to test research hypotheses and adopts structural equation modeling to validate the whole conceptual model.

The research findings first reveal two trust mechanisms linking small talk and knowledge sharing. Meanwhile, the perceived similarity between employees, specifically, strengthens the affective pathway of trust rather than the cognitive pathway of trust.

This study combines Interaction Ritual Theory and constructs a dual-facilitating pathway approach that aims to reveal the impact of small talk on knowledge sharing, describing how and when small talk could generate a positive effect on knowledge sharing. This research provides intriguing and dynamic insights into understanding knowledge sharing processes.

The increasing popularity of music streaming services also increases the need to customize the services for each user to attract and retain customers. Most of the music streaming services will not have explicit ratings for songs; they will have only implicit feedback data, i.e user listening history. For efficient music recommendation, the preferences of the users have to be infered, which is a challenging task.

Preferences of the users can be identified from the users' listening history. In this paper, a hybrid music recommendation system is proposed that infers features from user's implicit feedback and uses the hybrid of content-based and collaborative filtering method to recommend songs. A Content Boosted K-Nearest Neighbours (CBKNN) filtering technique was proposed, which used the users' listening history, popularity of songs, song features, and songs of similar interested users for recommending songs. The song features are taken as content features. Song Frequency–Inverse Popularity Frequency (SF-IPF) metric is proposed to find the similarity among the neighbours in collaborative filtering. Million Song Dataset and Echo Nest Taste Profile Subset are used as data sets.

The proposed CBKNN technique with SF-IPF similarity measure to identify similar interest neighbours performs better than other machine learning techniques like linear regression, decision trees, random forest, support vector machines, XGboost and Adaboost. The performance of proposed SF-IPF was tested with other similarity metrics like Pearson and Cosine similarity measures, in which SF-IPF results in better performance.

This method was devised to infer the user preferences from the implicit feedback data and it is converted as rating preferences. The importance of adding content features with collaborative information is analysed in hybrid filtering. A new similarity metric SF-IPF is formulated to identify the similarity between the users in collaborative filtering.

Anecdotal evidence suggests that casting actors with similar facial features in a movie can pose challenges in foreign markets, hindering the audience's ability to recognize and remember characters. Extending developments in the literature on the cross-race effect, we hypothesize that facial similarity – the extent to which the actors starring in a movie share similar facial features – will reduce the country-level box-office performance of US movies in East and South-East Asia (ESEA) countries.

We assembled data from various secondary data sources on US non-animation movies (2012–2021) and their releases in ESEA countries. Combining the data resulted in a cross-section of 2,616 movie-country observations.

Actors' facial similarity in a US movie's cast reduces its box-office performance in ESEA countries. This effect is weakened as immigration in the country, internet penetration in the country and star power increase and strengthened as cast size increases.

This first study on the effects of cast's facial similarity on box-office performance represents a novel extension to the growing literature on the antecedents of movies' box-office performance by being at the intersection of the two literature streams on (1) the box-office effects of cast characteristics and (2) the antecedents, in general, of box-office performance in the ESEA region.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

Facilitating members' continual participation in a community is crucial for ensuring the community's long-term survival. However, knowledge regarding whether member similarity is related to member participation and the mechanism underlying this relationship is limited. Drawing on similarity–attraction, social exchange and social identity theories, this study explored the influences of different facets of similarity (i.e. value, personality and goal similarity) on group norm conformity, group identity and social participation.

Data were collected from 444 Taiwanese members of social networking sites (SNSs), and structural equation modeling was employed to examine the hypothesized relationships.

The results revealed that value similarity directly affected group norm conformity but did not directly affect group identity; personality similarity influenced group identity but not group norm conformity. Goal similarity had positive influences on group norm conformity and group identity. Moreover, group norm conformity had direct and positive influences on group identity and social participation; group identity also had a positive influence on social participation.

On the basis of the aforementioned findings, this study contributes to the understanding of factors facilitating SNS members' participation from the perspective of similarity. These findings can serve as a reference for SNS administrators to facilitate social participation by emphasizing member similarity.

The purpose of this paper is to investigate the impact of two dimensions of technological relatedness, namely technological similarity and complementarity, on collaborative performance, plus the mediating role of collaboration network stickiness and the moderating role of partner expertise and geographical distance in interfirm collaboration contexts.

This study takes Chinese Scientific and Technological Achievements (STA) of inter-firm collaboration in five high-tech fields in 2010–2020 as the sample and uses OLS regression to test the hypothesis.

Technological similarity and complementarity positively affect collaborative performance. Partner expertise negatively moderates the relationship between similarity, complementarity and collaborative performance. Geographical distance positively moderates the relationship between similarity and collaborative performance while negatively moderates that between complementarity and collaborative performance. Collaboration network stickiness partly mediates the relationship between similarity and collaborative performance.

This study expands literature on inter-firm collaboration, especially research on the antecedents of collaborative performance. Moreover, this study not only compensates for lack of empirical analysis in partner selection research, but also utilizes second-hand data to enhance the objectivity of analysis. Additionally, we enrich the research on the moderating role of partner expertise and geographical distance as well as the mediating role of collaboration network stickiness.

Supply chain (SC) professionals are increasingly working alongside business partners of diverse backgrounds, which has been argued to engender both innovation and creativity but also found as potentially detrimental to SC relationships and performance. To reconcile these views, this study explores two mechanisms – supplementary (similarity) and complementary fits – at the surface (observable traits) and deep (unobservable characteristics) levels and their impact on a focal firm representative’s perception of a SC partner’s trustworthiness.

Model was tested using survey data from 285 managers involved in interorganizational SC relationships.

Results indicate that a focal firm representative’s perception of supplementary and complementary fits with a SC partner positively impacts their perception of the partner’s trustworthiness. However, the effects of similarity at both surface and deep levels and complementarity weaken each other.

Understanding the mechanisms of diversity in SC relationships is crucial for fostering trustworthiness and achieving organizational objectives. Firms should evaluate both supplementary and complementary fits when hiring or assigning roles. Embracing a complementary fit not only promotes diversity but also mitigates the negative impact of similarity bias, ultimately strengthening trustworthiness within the organization's SC ecosystem.

By simultaneously examining individual and combined effects of two unique mechanisms of supplementarity and complementarity at the surface and deep levels, this study sheds light on inconsistent findings of the effects of diversity in the SCM literature.

Joint advertising is an emerging strategy where marketers promote both brands in the same marketing communication. This research determines how the domestic, foreign, or global nature of the partner impacts important brand-related outcomes and identifies underlying psychological process mechanisms and contextual variables that affect this relationship.

Across three experiments, we investigate how the type of joint advertising partner impacts consumer attitudes and behaviors. We establish the number of similarities between the partners and perceived cognitive fit as the mediating process mechanisms underlying this relationship, with holistic processing moderating the effect.

We find that when consumers are exposed to joint advertisements between domestic or global [foreign] brands, they will be able to generate more [fewer] similarities between the partners and perceive a stronger [weaker] cognitive fit. Moreover, these similarities interact with consumer cultural traits related to holistic processing style to differentially influence perceived cognitive fit and downstream consumer attitudes and behaviors.

Partnering for mutually beneficial, joint advertisements is a growing phenomenon that redefines traditional thinking about advertising, but the success of the joint advertisement is contingent upon the characteristics and compatibility of the partners.

We examine the relationships between clients’ level of coaching readiness and trust in their executive coach and increases to both personal learning improved work performance. Distance relationships, the setting for this study, epitomize the norms of the New World of Work (NWoW), but also provide particular challenges for building trust and recognizing similarities between client and coach.

This study investigates distance coaching relationships in matched-pairs, longitudinal investigation of formal executive coaching.

Results support the proposed moderated mediation path. Findings reveal that both coaches’ perceptions of client readiness for coaching and client trust in coach each predict both client personal skill development and performance improvement.

While important toward gaining a better understanding of the relational functioning of distance coaching relationships, inclusion of only distance relationships may truncate the generalizability of our findings.

The study’s findings have practical implications for organizations that invest in executive coaching with regard to the importance of evaluating the candidates' readiness for coaching before the assignment, trust-building throughout distance coaching relationships and perceptions of similarity on client coaching outcomes.

Distance relationships, the setting for this study, provide particular challenges for building trust and recognizing similarities between client and coach and the current investigation points to the relevance of these relational mechanisms to client outcomes. In so doing, this study explores how perceptions of deep-level similarity between a coach and client may serve as moderators of these relationships.