Cross-region collaborative filtering for new point-of-interest recommendation

Zheng, Ning; Jin, Xiaoming; Li, Lianghao

doi:10.1145/2487788.2487804

Cited by 11 publications

(8 citation statements)

References 3 publications

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“…Firstly, Ye et al [ 10 ] proposed a unified POI recommendation framework to provide a POI recommendation service for LBSNs, exploring user preference, social influence, and geographical influence. Later, Zheng et al [ 16 ] proposed the cross-region topic-based collaborative filtering (CRTCF) method based on hidden topics mined from user check-in records with the aim of recommending new POIs to a user in regions where he/she has rarely been before. In the same year, Liu et al [ 5 ] proposed a Geographical-Topical Bayesian Non-negative Matrix Factorization (GT-BNMF) model that allows capturing the geographical influences on user’s check-in behaviors, as well as integrating the POIs’ regional popularity.…”

Section: State Of the Artmentioning

confidence: 99%

“…In summary, according to Table 1 , it is concluded that only one approach [ 19 ] in addition to HyRA addresses ratings, nine of them address check-ins [ 5 , 6 , 7 , 10 , 15 , 16 , 17 , 18 , 20 ]; and one addresses both ratings and check-ins [ 21 ]. Geographical influence is the factor most used than social influence with eight [ 5 , 6 , 7 , 10 , 15 , 17 , 18 , 21 ] (in addition to HyRA) and four [ 10 , 16 , 18 , 20 ] works, respectively. The POI categories—six approaches [ 5 , 16 , 17 , 19 , 20 , 21 ] (in addition to HyRA)—are also more explored than social influence.…”

Section: State Of the Artmentioning

confidence: 99%

“…Geographical influence is the factor most used than social influence with eight [ 5 , 6 , 7 , 10 , 15 , 17 , 18 , 21 ] (in addition to HyRA) and four [ 10 , 16 , 18 , 20 ] works, respectively. The POI categories—six approaches [ 5 , 16 , 17 , 19 , 20 , 21 ] (in addition to HyRA)—are also more explored than social influence. Other characteristics that have been analyzed are temporal influence by four approaches [ 7 , 17 , 18 , 21 ] and POI popularity by one approach [ 20 ].…”

Section: State Of the Artmentioning

confidence: 99%

“…Most approaches used LBSN to collect data for the recommendation algorithm, only two approaches [ 3 , 19 ] and HyRA used other information sources. Cosine similarity is the most common measure in the recommendation approaches [ 10 , 16 , 18 , 19 , 20 , 21 ], Euclidean distance is used in [ 20 ], and Pearson correlation, Euclidean distance, Cosine similarity, Manhattan distance, and Chebyshev distance are used by HyRA. The user-based CF algorithm is employed in [ 10 , 19 , 21 ] using the Cosine similarity as a similarity measure while HyRA is the only approach that codes a user-based CF with an average aggregation operator as a similarity measure.…”

Section: State Of the Artmentioning

confidence: 99%

See 3 more Smart Citations

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

Alvarado-Uribe

Gómez-Oliva

Barrera-Animas

et al. 2018

Sensors

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Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on the Web such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI) for a specific user according to his/her preferences and the Smart POIs’ context. Hence, a novel Hybrid Recommendation Algorithm (HyRA) is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF) algorithm as well as including the Smart POIs’ categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs’ categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs’ categories are provided. The experimental results show that the recommendations suggested by HyRA are promising.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

See 2 more Smart Citations

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

Alvarado-Uribe

Gómez-Oliva

Barrera-Animas

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Yin [20] proposed LCA-LDA model by giving consideration to both personal interest and local preference. Zheng et al [22] proposed a cross-region collaborative filtering method based on hidden topics about check-in records to recommend new POIs. Zhang et al [23] distinguished the user preferences on the content of POIs from the POIs themselves and combined the predicted rating on content and location of POI.…”

Section: Related Workmentioning

confidence: 99%

CoSoLoRec: Joint Factor Model with Content, Social, Location for Heterogeneous Point-of-Interest Recommendation

Guo

Li²,

et al. 2016

Lecture Notes in Computer Science

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The pervasive use of Location-based-Social-Networks calls for more precise Point-of-Interest recommendation. The probability of a user's visit to a target place is influenced by multiple factors. Though there are several fusion models in such fields, heterogeneous information are not considered comprehensively. To this end, we propose a novel probabilistic latent factor model by jointly considering the social correlation, geographical influence and users preference. To be specific, a variant of Latent Dirichlet Allocation is leveraged to extract the topics of both user and POI from reviews which is denoted as explicit interest. Then, Probabilistic Latent Factor Model is introduced to depict the implicit interest. Moreover, Kernel Density Estimation and friend-based Collaborative Filtering are leveraged to model users geographic allocation and social correlation respectively. Thus, we propose CoSoLoRec, a fusion framework, to ameliorate the recommendation. Experiments on two real-word datasets show the superiority of our approach over the state-of-the-art methods.

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Towards the Development of a Smart Tourism Application Based on Smart POI and Recommendation Algorithms: Ceutí as a Study Case

Alvarado-Uribe

Gómez-Oliva

Molina

et al. 2017

Innovative Mobile and Internet Services in Ubiquitous Computing

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Cross-region collaborative filtering for new point-of-interest recommendation

Cited by 11 publications

References 3 publications

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

CoSoLoRec: Joint Factor Model with Content, Social, Location for Heterogeneous Point-of-Interest Recommendation

Towards the Development of a Smart Tourism Application Based on Smart POI and Recommendation Algorithms: Ceutí as a Study Case

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