POI Recommendation of Location-Based Social Networks Using Tensor Factorization

2018 ◽  
Author(s):  
Guoqiong Liao ◽  
Shan Jiang ◽  
Zhiheng Zhou ◽  
Changxuan Wan ◽  
Xiping Liu
Keyword(s):  
Social Networks ◽  
Tensor Factorization ◽  
Poi Recommendation ◽  
Location Based Social Networks

scholarly journals Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation

2018 ◽  
Author(s):  
Hao Wang ◽  
Huawei Shen ◽  
Wentao Ouyang ◽  
Xueqi Cheng
Keyword(s):  
Social Networks ◽  
Real World ◽  
Fundamental Problem ◽  
Physical Distance ◽  
Experimental Results ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Movie Recommendation ◽  
Real World Datasets ◽  
Location Based Social Networks

Point-of-interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing methods model the geographical influence between two POIs as the probability or propensity that the two POIs are co-visited by the same user given their physical distance. These methods assume that geographical influence between POIs is determined by their physical distance, failing to capture the asymmetry of geographical influence and the high variation of geographical influence across POIs. In this paper, we exploit POI-specific geographical influence to improve POI recommendation. We model the geographical influence between two POIs using three factors: the geo-influence of POI, the geo-susceptibility of POI, and their physical distance. Geo-influence captures POI?s capacity at exerting geographical influence to other POIs, and geo-susceptibility reflects POI?s propensity of being geographically influenced by other POIs. Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation.

scholarly journals Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation

2021 ◽  
Author(s):  
Yang Li ◽  
Tong Chen ◽  
Yadan Luo ◽  
Hongzhi Yin ◽  
Zi Huang
Keyword(s):  
Social Networks ◽  
State Of The Art ◽  
User Preference ◽  
Sequential Patterns ◽  
Preference Modelling ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Multi Level ◽  
Real World Datasets ◽  
Location Based Social Networks

Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec), which jointly learns POI embeddings and infers a user's temporal preferences from the graph-augmented POI sequence. To overcome the sparsity of POI-level interactions, we further infuse category-awareness into SGRec with a multi-task learning scheme that captures the denser category-wise transitions. As such, SGRec makes full use of the collaborative signals for learning expressive POI representations, and also comprehensively uncovers multi-level sequential patterns for user preference modelling. Extensive experiments on two real-world datasets demonstrate the superiority of SGRec against state-of-the-art methods in next POI recommendation.

scholarly journals Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks

2021 ◽  
Vol 10 (1) ◽  
pp. 36
Author(s):  
Hang Zhang ◽  
Mingxin Gan ◽  
Xi Sun
Keyword(s):  
Social Networks ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Repeat Visit ◽  
Preference Evolution ◽  
Attenuation Mechanism ◽  
Mobility Behavior ◽  
Location Based Social Networks

In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods.

Fused Collaborative Filtering With User Preference, Geographical and Social Influence for Point of Interest Recommendation

2019 ◽  
Vol 16 (4) ◽  
pp. 40-52 ◽  
Author(s):  
Jun Zeng ◽  
Feng Li ◽  
Xin He ◽  
Junhao Wen
Keyword(s):  
Social Networks ◽  
User Preference ◽  
Integrated Analysis ◽  
Geographical Distance ◽  
Point Of Interest ◽  
Social Reputation ◽  
Poi Recommendation ◽  
Interest Level ◽  
Location Based Social Networks ◽  
Better Than

Point of interest (POI) recommendation is a significant task in location-based social networks (LBSNs), e.g., Foursquare, Brightkite. It helps users explore the surroundings and help POI owners increase income. While several researches have been proposed for the recommendation services, it lacks integrated analysis on POI recommendation. In this article, the authors propose a unified recommendation framework, which fuses personalized user preference, geographical influence, and social reputation. The TF-IDF method is adopted to measure the interest level and contribution of locations when calculating the similarity between users. Geographical influence includes geographical distance and location popularity. The authors find friends in Brightkite share low common visited POIs. It means friends' interests may vary greatly. Instead of directly getting recommendations from so-called friends in LBSN, the users attain recommendation from others according to their reputation. Finally, experimental results on real-world dataset demonstrate that the proposed method performs much better than other recommendation methods.

scholarly journals Discovering Travel Community for POI Recommendation on Location-Based Social Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Tang ◽  
Dandan Cai ◽  
Zongtao Duan ◽  
Junchi Ma ◽  
Meng Han ◽  
...  
Keyword(s):  
New York ◽  
Social Networks ◽  
Transportation Systems ◽  
Variable Order ◽  
Linear Discriminant ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Recommendation Algorithms ◽  
Real World Datasets ◽  
Location Based Social Networks

Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs). The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable-order Markov model to determine the distribution of a user’s POIs based on his or her travel histories in LBSNs. To further enhance the user’s experience, we also apply linear discriminant analysis to cluster the topics related to “Travel” and connect to users with social links or similar interests. The probability of POIs based on users’ historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users.

Sign in / Sign up

Export Citation Format

Share Document