UTSP: User-Based Two-Step Recommendation With Popularity Normalization Towards Diversity and Novelty

Niu, Ke; Zhao, Xiangyu; Li, Fangfang; Li, Ning; Peng, Xueping; Chen, Wei

doi:10.1109/access.2019.2939945

Cited by 12 publications

(5 citation statements)

References 39 publications

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“…Existing approaches mainly focus on spatio-temperal contexts [13][14][15] and recommendation models [16][17][18][19][20]. And there are some works on the framework of two-stage or two-step recommendation [21][22][23][24][25][26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

“…The two-stage or two-step recommendation strategy has already applied in news recommendation [21,22], movies recommendation [23,24], e-commerce [25,26], Automatic playlist continuation [27], conversational recommendation [28], Session-based Recommendation [29], Cross-market Recommendation [30], etc. Knox et al [21] proposed a scalable two-stage personalized news recommendation approach with a two-level representation in order to balance the novelty and diversity of the recommended result.…”

Section: Two-stage or Two-step Recommendationmentioning

confidence: 99%

“…Knox et al [21] proposed a scalable two-stage personalized news recommendation approach with a two-level representation in order to balance the novelty and diversity of the recommended result. Niu et al [22] proposed a user based two-step recommendation algorithm with popularity normalization to improve recommendation diversity and novelty. Zhao, et al [23,24] proposed the two-step recommendation approach of firstly recommending items and secondly finding high quality items which users like.…”

Section: Two-stage or Two-step Recommendationmentioning

confidence: 99%

“…Researchers and corporations often concentrate on spatio-temporal contexts [13][14][15] and recommendation models [16][17][18][19][20]. Besides, a few two-stage recommender systems have been proved to obtain the balance between accuracy and efficiency [21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

Liu¹,

Qing²,

Zhao³

et al. 2023

Computer Systems Science and Engineering

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Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads. But how to alleviate sparsity and further enhance accuracy is still challenging. Addressing at these issues, we propose to fuse spatio-temporal contexts into deep factorization machine (STC_DeepFM) offline for pick-up area recommendation, and within the area to recommend pick-up points online using factorization machine (FM). Firstly, we divide the urban area into several grids with equal size. Spatio-temporal contexts are destilled from pick-up points or points-of-interest (POIs) belonged to the preceding grids. Secondly, the contexts are integrated into deep factorization machine (DeepFM) to mine high-order interaction relationships from grids. And a novel algorithm named STC_DeepFM is presented for offline pick-up area recommendation. Thirdly, we devise the architecture of offline-to-online (O2O) recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and efficiency. Some experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts, different recommendation models, and the O2O architecture. The results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods, and the O2O architecture achieves excellent real-time performance.

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Section: Related Workmentioning

confidence: 99%

Section: Two-stage or Two-step Recommendationmentioning

confidence: 99%

Section: Two-stage or Two-step Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

Liu¹,

Qing²,

Zhao³

et al. 2023

Computer Systems Science and Engineering

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“…The efficaciousness of the proposed scheme is evaluated on the Non-spatial image attribute extraction, which contains 55 samples that performing different universal separation. After extraction, extracted features like seed, skin, rind, and pomace are normalized and concatenated into the one-dimensional feature vector for each observation [66]. According to the 3D sensor pattern-based semi-automatic separation, the images are divided into observations as labeled and incre-…”

Section: Accuracymentioning

confidence: 99%

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

Sanjeevi

Kumar

Prasanna

et al. 2020

Complex Intell. Syst.

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Constituting the agriculture solid substance manufacture, the post-harvest sector processing schema is direct to preventing reduce the losses in intelligent agriculture. Many processing schemata will be preventing post-harvest losses on the agriculture solid substance manufacture, especially sekai-ichi apple is the regularly used fruit also used to make active in human-related activities of the sensory and control function consisting of an agricultural industry. Sekai-ichi apple is being a definite number of diseases induce, but it is to the highest degree of wastage involving in the Post-Harvest process. Especially sekai-ichi apple count loss is an unsafe many time because it not critically post-harvest. Regardless of consideration, the existing hierarchical model specified post-harvest losses prevention research has deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. This paper suggests a "Hierarchical Model within Ontology Enabled IoT" for distinguishable healthy separation of sekai-ichi apple by using Boosted Continuous Non-spatial whole Attribute Extraction (BCNAE). Sekai-ichi apple count loss is always safe on critically post-harvest. Proposed Post-Harvest hierarchical model specified post-harvest losses prevention and deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. In these suggestions, the separation cognitive operation takes the three levels of processing schemes such as lower level, middle level, and higher level. Firstly, the lower level is express agreements with the dynamic functioning for maintaining the definite number of manual induces. This lower level showing an absorption with the activity of manual separation by the human reliability determination. Secondly, the middle level is an express arrangement with the dynamic functioning for reducing the overfitting and accommodate to fitting the right shape deliberation. Middle level is establishing being generalized by concentrating the time-varying features in the occurrence of a change for the worse identification. Finally, the upper level is express for features refining with the help of the function of sekai-ichi apple image segmentation connection. This interpretability process helps to make the proven position of a prominent classification in a particular fruit on the agriculture solid substance. These three processing flow constructs the ontology structure with manually collected sekai-ichi apple images from a 3D sensor. The observational consequences express that the proposed BCNAE framework recognizes a detection performance carrying out with an optimized-separation ratio for time-variant of the separation process.

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Top-N music recommendation framework for precision and novelty under diversity group size and similarity

2023

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UTSP: User-Based Two-Step Recommendation With Popularity Normalization Towards Diversity and Novelty

Cited by 12 publications

References 39 publications

Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

Top-N music recommendation framework for precision and novelty under diversity group size and similarity

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