The next Point-of-Interest (POI) recommendation is a hotspot for both industry and academia, which helps users better experience the physical world. However, existing methods suffer from a severe bias towards recommending repeat POIs that have been visited by the target user before, and perform inefficiently when recommending new POIs that have not been visited by the target user yet. To overcome this issue, we delve into the next new POI recommendation and uncover the coexistence of local and global exploration patterns in users’ visits to new POIs, showing their willingness to explore not only nearby new POIs but also those distant ones. Subsequently, we develop a novel Local and Global Exploration ( LGE ) framework for the next new POI recommendation. In particular, LGE involves three key modules: (1) a Zone-Aware Local Exploration (ZLE) module, which encourages users to explore POIs in the local area by learning zone-aware POI representations and regularizing POI prediction with zone information; (2) an Intention-Aware Global Exploration (IGE) module, which recommends POIs that meet user intentions without distance constraints by extracting static and dynamic intentions from category information; (3) a fusion module, which contains a Mean Pooling (MP) strategy and a Weighted Pooling (WP) strategy to aggregate the outputs of local and global exploration modules for the final recommendation. Experiments carried out on real-world datasets have shown the effectiveness of LGE in recommending new POIs.

Existing sequential Point of Interest (POI) recommendation methods overlook a fact that each city exhibits distinct characteristics and totally ignore the city signature. In this study, we claim that city matters in sequential POI recommendation and fully exploring city signature can highlight the characteristics of each city and facilitate cross-city complementary learning. To this end, we consider the two-city scenario and propose a Dual-Target Cross-City Sequential POI Recommendation model (DCSPR) to achieve the purpose of complementary learning across cities. On one hand, DCSPR respectively captures geographical and cultural characteristics for each city by mining intra-city regions and intra-city functions of POIs. On the other hand, DCSPR builds a transfer channel between cities based on intra-city functions, and adopts a novel transfer strategy to transfer useful cultural characteristics across cities by mining inter-city functions of POIs. Moreover, to utilize these captured characteristics for sequential POI recommendation, DCSPR involves a new region- and function-aware network for each city to learn transition patterns from multiple views. Extensive experiments conducted on two real-world datasets with four cities demonstrate the effectiveness of DCSPR .

One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems. Current novel recommendation methods over-emphasize the importance of tail items without differentiating the degree of users’ intent on popularity and often incur a sharp decline of accuracy. Moreover, none of existing studies has ever taken the extreme case of tail items, i.e., cold-start items without any interaction, into consideration. In this work, we first disclose the mechanism that drives a user's interaction towards popular or niche items by disentangling her intent into conformity influence (popularity) and personal interests (preference). We then present a unified end-to-end framework to simultaneously optimize accuracy and novelty targets based on the disentangled intent of popularity and that of preference. We further develop a new paradigm for novel recommendation of cold-start items which exploits the self-supervised learning technique to model the correlation between collaborative features and content features. We conduct extensive experiments on three real-world datasets. The results demonstrate that our proposed model yields significant improvements over the state-of-the-art baselines in terms of the trade-off between accuracy and novelty.

The Point-of-Interest (POI) transition behaviors could hold absolute sparsity and relative sparsity very differently for different cities. Hence, it is intuitive to transfer knowledge across cities to alleviate those data sparsity and imbalance problems for next POI recommendation. Recently, pre-training over a large-scale dataset has achieved great success in many relevant fields, like computer vision and natural language processing. By devising various self-supervised objectives, pre-training models can produce more robust representations for downstream tasks. However, it is not trivial to directly adopt such existing pre-training techniques for next POI recommendation, due to thelacking of common semantic objects (users or items) across different cities. Thus in this paper, we tackle such a new research problem ofpre-training across different citiesfor next POI recommendation. Specifically, to overcome the key challenge that different cities do not share any common object, we propose a novel pre-training model namedCATUS, by transferring thecategory-leveluniversal transition knowledge over different cities. Firstly, we build two self-supervised objectives inCATUS:next category predictionandnext POI prediction, to obtain the universal transition-knowledge across different cities and POIs. Then, we design acategory-transition oriented sampleron the data level and animplicit and explicit transfer strategyon the encoder level to enhance this transfer process. At the fine-tuning stage, we propose adistance oriented samplerto better align the POI representations into the local context of each city. Extensive experiments on two large datasets consisting of four cities demonstrate the superiority of our proposedCATUSover the state-of-the-art alternatives. The code and datasets are available at https://github.com/NLPWM-WHU/CATUS.

Recommendation systems have been widely applied to many E-commerce and online social media platforms. Recently, sequential item recommendation, especially session-based recommendation, has aroused wide research interests. However, existing sequential recommendation approaches either ignore the historical sessions or consider all historical sessions without any distinction that whether the historical sessions are relevant or not to the current session, which motivates us to distinguish the effect of each historical session and identify relevant historical sessions for recommendation. In light of this, we propose a novel deep learning based sequential recommender framework for session-based recommendation, which takes Nonlocal Neural Network and Recurrent Neural Network as the main building blocks. Specifically, we design a two-layer nonlocal architecture to identify historical sessions that are relevant to the current session and learn the long-term user preferences mostly from these relevant sessions. Besides, we also design a gated recurrent unit (GRU) enhanced by the nonlocal structure to learn the short-term user preferences from the current session. Finally, we propose a novel approach to integrate both long-term and short-term user preferences in a unified way to facilitate training the whole recommender model in an end-to-end manner. We conduct extensive experiments on two widely used real-world datasets, and the experimental results show that our model achieves significant improvements over the state-of-the-art methods.

Signed directed networks with positive or negative links convey rich information such as like or dislike, trust or distrust. Existing work of sign prediction mainly focuses on triangles (triadic nodes) motivated by balance theory to predict positive and negative links. However, real-world signed directed networks can contain a good number of "bridge'' edges which, by definition, are not included in any triangles. Such edges are ignored in previous work, but may play an important role in signed directed network analysis.%Such edges serve as fundamental building blocks and may play an important role in signed network analysis.