Multimodal data can more comprehensively portray changes in user interests, and thus, multimodal sequential recommendation (MSRS) has gained widespread attention in recent years. However, the MSRS faces two key challenges: (1) how to effectively model long-range dependencies in user interaction sequence; and (2) how to efficiently fuse multimodal features. To address these challenges, this article proposes a novel multimodal sequential recommendation architecture based on pure convolutional neural network (CNN), named PCMSRec. PCMSRec contains two key innovations: first, by using the global receptive field of large kernel convolution, it models the long-range dependencies of multimodal user interaction sequence, breaking through the limitation that existing CNN-based methods can only capture local short-distance dependencies; second, by taking advantage of the high flexibility of the CNN architecture, it models the relationships among multimodal features of items through a carefully designed convolutional layer architecture and fusion strategy. Specifically, PCMSRec consists of two blocks: sequence-feature block and modal block. The sequence-feature block models long-range dependencies in user interaction sequence through large kernel convolutional layer and extracts item features by incorporating a bottleneck architecture. The modal block models the complex relationships between multimodal features using multiple convolutional layer. Experimental results on five public datasets show that PCMSRec outperforms existing methods.

Multimodal sequential recommendation extracts multimodal features from user interaction sequences to improve user sequence modeling accuracy and expressiveness. However, this field still faces two major challenges: (1) how to effectively improve multimodal features synergy in user interaction sequences, and (2) how to reduce the number of model parameters while ensuring recommendation performance. To address these challenges, this article proposes a memory-enhanced multimodal sequential recommendation method with multiple distillation (M3DSRec), which includes four key designs: First, a parameter standardization mixture of experts is used to mitigate multimodal feature distribution imbalance and avoid inter-feature interference. Second, a main-modal cooperative block is designed to fuse features of other modalities based on the primary unimodal feature, thereby enhancing the synergy among multimodal features. Third, a memory-enhanced dynamic cluster is designed, which uses a memory bank to enhance the representation of multimodal features in user interaction sequences. Finally, a multiple distillation strategy is used to align the features and logits of the teacher and student networks at multiple levels. Experimental results on five public datasets show that M3DSRec achieves a balance between recommendation performance and the number of model parameters, with both the teacher and student networks outperforming existing mainstream methods.

The Next POI recommendation, which has attracted many attentions recently, is a complex study due to the sparsity of check-in data and numerous sequential patterns. The recent methods based on sequential modeling have shown promising applicability for this task. However, most of existing next POI recommendation researches only model users’ preferences based on their own sequences and ignore the influence of partners who visit POI with the target user and may change with time. Inspired by dynamic Graph neural networks, we propose a Group-aware Dynamic Graph Representation Learning (GDGRL) method for next POI recommendation. GDGRL connects different user sequences and specific partners via dynamic graph structure, which contains interactions between users and POIs as well as influence of partners. The users’ dynamic preferences are learned from group-aware dynamic graph and context-aware dynamic graph through dynamic graph neural networks. Finally, the next POI recommendation task is transformed into a link prediction between user node and POI node in the dynamic graph. Extensive experiments on two real-world datasets show that GDGRL outperforms several state-of-the-art approaches.

Recommendations for events play a pivotal role in facilitating the discovery of upcoming intriguing events within Event-Based Social Networks (EBSNs). Previous research has established the crucial significance of mining contextual features and implicit relationships to enhance recommendation performance and alleviate data sparsity issues. However, the noise inherent in contextual features exacerbates data sparsity and hampers the ability of previous methods to explore implicit relationships for mitigating data sparsity. To address this challenge, we propose a variational type graph autoencoder model that attenuates the influence of noise in different types of context features by introducing type-specific latent variables. First, we introduce a heterogeneous denoising convolution module composed of two components: (1) Denoising attention aggregation is proposed to mitigate the influence of noisy structures and uncover implicit relationships. (2) A heterogeneous normalization module leverages context features within the same type to alleviate the effects of noise in context features and data sparsity. Furthermore, we propose a learnable heterogeneous mixture prior that assists in assigning different priors to distinct types of latent variables, effectively modeling different types of contextual features. Through comprehensive experiments conducted on real-world datasets, we demonstrate the compelling performance of our model compared to state-of-the-art competitive approaches.

Point of interest (POI) recommendation has received significant attention in recent years, most existing studies exploit multiple auxiliary information to alleviate the problem of data sparsity, and consider the sequential features of user mobility. However, few studies consider the category-level characteristics derived from each user's historical check-in frequencies, and the characteristics between different latent factors. In this paper, we exploit category-level multiple characteristics to generate recommendation. First, we obtain the frequency characteristics by in-depth analysis of the historical check-in frequencies for different categories by each user, and propose a scheme including KL-divergence and text analysis algorithm. Then we propose a category-level sequential- and non-sequential influence-aware probabilistic generative model (CSNS), which models the characteristics (correlation and indeterminate decisiveness) between user latent behavior topics and latent sequence patterns. We design two stages to generate recommendations. In the first stage, CSNS and frequencies characteristics are exploited jointly to recommend the POI categories that users may visit. In the second stage, we depend on user profiles and poi features, and sort the candidate POI sets by combining the POI categories provided in the first stage. Comprehensive experiments on two real-world datasets demonstrate that our method outperforms the existing state-of-the-art POI recommendation models.

In recent years, context-aware recommender systems (CARSs), which incorporate contextual information to achieve better recommendations, become a hot topic in the domain of recommender systems. Many context-aware recommendation methods have been proposed in the past decades. Some literatures provide survey of the research on CARSs. However, they mainly focus on context-aware recommendation methods and overlook the evaluation of them. The evaluation methods, evaluation properties and datasets of CARSs are different from those of traditional recommender systems where contexts are not considered. In this paper, we provide a review for evaluation of CARSs. We will introduce the basic concepts of CARSs, propose a new dataset partition method for each category of CARSs according to our classification of CARSs, summarize the evaluation method. Then we summarize the evaluation properties that CARSs pays attention to, which are different from the NCARSs. In addition, we also summarize the datasets specifically for CARSs, its applicable CARSs type and its evaluation dimensions and metrics. Based on our review, we draw some conclusions from evaluation perspective and point out future research directions.

Location-based social networks (LBSNs) have become a popular platform for users to share their activities with friends and families, which provide abundant information for us to study issues of group venue recommendation by utilizing the characteristics of check-in data. Although there are some studies on group recommendation for venues, few studies consider the group’s venue preference in different temporal patterns. In this article, we discover that the group’s activity venue has a temporal effect, that is, the group’s preference for the activity venue is different at different times. For example, a couple of lovers prefer to travel to tropical regions in winter and relax in bars in the evening. Based on this discovery, we present a Time-aware Multi-pattern (TaMp) topic model to capture the group’s interest in the activity venue in multiple temporal patterns (including the daily pattern, the weekly pattern, the monthly pattern, and the quarterly pattern). The TaMp model takes into account the topic, members, temporality, and venue information of group activities and the latent relations among them, especially the strong correlation between the activity time and the corresponding activity venue. Then, we propose a group venue recommendation method based on the TaMp model. In addition, an improved grouping algorithm (iGA) in LBSNs is put forward to enhance the rationality of grouping and the accuracy of group venue recommendation. We conduct comprehensive experiments to evaluate the performance of TaMp on two real-world datasets. The results show that our proposed method outperforms the state-of-the-art group venue recommendation and demonstrates the significance of temporal effects in explaining group activities.

Personalized venues recommendation is essential to help people find attractive venue to visit as growth of location-based social networks. Existing approaches never distinguish user individual interests from her social preferences, which leads to a bottleneck of modeling user check-in behaviors accurately. In this paper, we find the differences between user interests and her social preferences clearly and investigate the time law of user check-in behaviors in depth. Consequently, we propose a social-period-aware topic model (SPATM) to learn the influence weights of both user interests and her social preferences on making-decision for each check-in time automatically. Especially, we model latent topic by leveraging smaller size of dynamic activities instead of static categories, which can alleviate the data sparsity problem by using more co-occurrent activities information. Moreover, our approach can automatically judge whether a user’s social preference is periodic or aperiodic and learn the periodicity of periodic one. Furthermore, the Alias Sampling based training approach is introduced to improve sampling efficiency. The results demonstrate our proposed model is effective and outperforms the state-of-the-art approaches in terms of effectiveness and efficiency. Besides, SPATM can learn semantically coherent latent topics and geographically dispersed latent social topics which are useful to explain recommendation.

Newly emerging Event-based Social Network (EBSN) concentrates on connecting both online social relationships and offline local events. For the growing amount of events published on EBSNs, personalized event recommendation becomes essential to help users choose attractive events. But most of existing event recommendation algorithms fail to distinguish constraint factors of users’ event participation behaviors from preference factors, which reflects the cost of event participation that hinders users from attending interested events. To take full advantage of influences from contextual information on users’ event participation, we differentiate preference and constraint factors which contribute to users’ decision for event participation, and extract the soft spatial and temporal constraints from event venue and start time contexts respectively. Then we propose the Preference and Constraint Factor Model (PCFM) based on factorization machine model, using attentive mechanism to weight feature interactions and incorporate latent factors of users and contextual features for personalized perference modeling and event recommendation. Moreover, learning-to-rank techniques are utilized to train PCFM as a ranking model for the implicit feedback nature of responses from users. Extensive experiments evaluate the performance of our proposed recommendation model on real-world EBSN datasets, and demonstrate the outperformance than state-of-art event recommendation methods on many metrics.

POI recommendation has become an essential means to help people discover attractive places. Intuitively, activities have an important impact on users’ decision-making, because users select POIs to attend corresponding activities. However, many existing studies ignore the social motivation of user behaviors and regard all check-ins as influenced only by individual user interests. As a result, they cannot model user preferences accurately, which degrades recommendation effectiveness. In this article, from the perspective of activities, this study proposes a probabilistic generative model called STARec. Specifically, based on the social effect of activities, STARec defines users’ social preferences as distinct from their individual interests and combines these with individual user activity interests to effectively depict user preferences. Moreover, the inconsistency between users’ social preferences and their decisions is modeled. An activity frequency feature is introduced to acquire accurate user social preferences because of close correlation between these and the key impact factor of corresponding check-ins. An alias sampling-based training method was used to accelerate training. Extensive experiments were conducted on two real-world datasets. Experimental results demonstrated that the proposed STARec model achieves superior performance in terms of high recommendation accuracy, robustness to data sparsity, effectiveness in handling cold-start problems, efficiency, and interpretability.

Event recommendation is essential to help people find attractive events to attend, but it intrinsically faces cold-start problem. The previous studies exploit multiple contextual factors to overcome the cold-start problem in event recommendation. However, they do not consider the correlation among different contextual factors. Moreover, suggesting events for a group of users also has not been well studied. In this paper, we first discover the correlation between organizer and textual content, i.e., the events held by the same organizer tend to have more similar content. Based on this observation, we present a content-venue-aware topic model (CVTM) to capture group interests on an event from two perspectives: content and venue. The correlation between organizer and content is modeled in CVTM to alleviate the sparsity of textual content, and then we can further extract group interests on content of an event more accurately. Finally, a group event recommendation method using CVTM is proposed. We conduct comprehensive experiments to evaluate the recommendation performance of our model on two real-world datasets. The results demonstrate that the proposed model outperforms the state-of-the-art methods that suggest upcoming events for groups. Besides, CVTM can learn semantically coherent latent topics which are useful to explain recommendations.

Event recommendation is an essential means to enable people to find attractive upcoming social events, such as party, exhibition, and concert. While growing line of research has focused on suggesting events to individuals, making event recommendation for a group of users has not been well studied. In this paper, we aim to recommend upcoming events for a group of users. We formalize group recommendation as a ranking problem and propose a group event recommendation framework GERF based on learning-to-rank technique. Specifically, we first analyze different contextual influences on user's event attendance, and extract preference of user to event considering each contextual influence. Then, the preference scores of the users in a group are taken as the features for learningto-rank to model the preference of the group. Moreover, a fast pairwise learning-to-rank algorithm, Bayesian group ranking, is proposed to learn ranking model for each group. Our framework is easily to incorporate additional contextual influences, and can be applied to other group recommendation scenarios. Extensive experiments have been conducted to evaluate the performance of GERF on two real-world datasets and demonstrate the appealing performance of our method on both accuracy and time efficiency.

News recommendation has become an essential way to help readers discover interesting stories. While a growing line of research has focused on modeling reading preferences for news recommendation, they neglect the instability of reader consumption behaviors, i.e., consumption behaviors of readers may be influenced by other factors in addition to user interests, which degrades the recommendation effectiveness of existing methods. In this article, we propose a probabilistic generative model, BoRe, where user interests and crowd effects are used to adapt to the instability of reader consumption behaviors, and reading sequences are utilized to adapt user interests evolving over time. Further, the extreme sparsity problem in the domain of news severely hinders accurately modeling user interests and reading sequences, which discounts BoRe’s ability to adapt to the instability. Accordingly, we leverage domain-specific features to model user interests in the situation of extreme sparsity. Meanwhile, we consider groups of users instead of individuals to capture reading sequences. Besides, we study how to reduce the computation to allow online application. Extensive experiments have been conducted to evaluate the effectiveness and efficiency of BoRe on real-world datasets. The experimental results show the superiority of BoRe, compared with the state-of-the-art competing methods.

With the rapid growth of social tagging systems, many efforts have been put on tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep neural network approach to solve this problem by mapping both the tag-based user and item profiles to an abstract deep feature space, where the deep-semantic similarities between users and their target items (resp., irrelevant items) are maximized (resp., minimized). Due to huge numbers of online items, the training of this model is usually computationally expensive in the real-world context. Therefore, we introduce negative sampling, which significantly increases the model's training efficiency (109.6 times quicker) and ensures the scalability in practice. Experimental results show that our model can significantly outperform the state-of-the-art baselines in tag-aware personalized recommendation: e.g., its mean reciprocal rank is between 5.7 and 16.5 times better than the baselines.