Take a Fresh Look at Recommender Systems from an Evaluation Standpoint

Sun, Aixin

doi:10.1145/3539618.3591931

Cited by 15 publications

(2 citation statements)

References 39 publications

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“…Evaluating recommendation systems is a crucial stage in validating their usage. Consequently, several studies have investigated the impact of time on online evaluations [31] [32] [33] because this temporal factor aids in interpreting the acceptance of the obtained recommendation results [34]. Online evaluation is the best method to evaluate RSs and CARS.…”

Section: Literature Reviewmentioning

confidence: 99%

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

HADJHENNI,

DENNOUNI,

SLAMA

2024

IJCDS

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Intelligent tourism can increase the interest of nomadic tourists in discovering new cities. However, many Points of Interest (POIs) are available, creating an information overload for tourists when choosing POIs to visit. For this reason, CARS (Context-Aware Recommendation Systems) can play an important role by exploiting the experiences of previous tourists and their contexts to recommend attractive POIs. Consequently, choosing the right POI recommendation algorithm (RA) for CARS is crucial because it involves the costly intervention of real tourists during the test phase. In order to make this phase more cost-effective, we can test several RAs simultaneously in order to assess their limitations in terms of cold start and tourist satisfaction. To compare these RAs, we propose in this article an approach called SEPRA (Systematic Evaluation for POI Recommendation Algorithms), which allows us to carry out an initial online evaluation of each tourist during their visit and a second offline evaluation of each CARS after the end of each POI path. To achieve this objective, we designed and implemented a new smart tourism tool that makes POI recommendations using two algorithms: the first is based on tourist/tourist similarity, and the second uses POI/POI similarity. These algorithms use memory-based collaborative filtering and are executed in parallel by our tool in the form of CARSs, incorporating time or weather as context variables. To evaluate these systems during their test phases, Our approach enables: (1) the calculation of prediction accuracy; (2) the examination of the relevance of the recommended POIs; and (3) the estimation of the acceptance rate of the recommendation process. Finally, the experimental results obtained with our approach show that the algorithm based on tourist similarity is more resistant to the cold start problem during the test phase and has a better satisfaction rate than the algorithm based on POI similarity.

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Section: Literature Reviewmentioning

confidence: 99%

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

HADJHENNI,

DENNOUNI,

SLAMA

2024

IJCDS

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“…In this light, user experience can be potentially enhanced by advancing the traditional recommender paradigms via generative models. This workshop provides a platform to facilitate the integration of generative models into recommender systems, with a focus on user modeling, content generation, interaction patterns, trustworthiness evaluations [23], and evaluation methods [13].…”

Section: Call For Papers 41 Introductionmentioning

confidence: 99%

The 2nd Workshop on Recommendation with Generative Models

Wang,

Zhang,

Lin

et al. 2024

Companion Proceedings of the ACM Web Conference 2024

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The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses on five key perspectives: (i) improving recommender algorithms, (ii) generating personalized content, (iii) evolving the user-system interaction paradigm, (iv) enhancing trustworthiness checks, and (v) refining evaluation methodologies for generative recommendations. With generative models advancing rapidly, an increasing body of research is emerging in these domains, underscoring the timeliness and critical importance of this workshop. The related research will introduce innovative technologies to recommender systems and contribute to fresh challenges in both academia and industry. In the long term, this research direction has the potential to revolutionize the traditional recommender paradigms and foster the development of next-generation recommender systems. CCS CONCEPTS• Information systems → Recommender systems.

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Handling Low Homophily in Recommender Systems With Partitioned Graph Transformer

Nguyen,

Weidlich

et al. 2025

IEEE Trans. Knowl. Data Eng.

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Take a Fresh Look at Recommender Systems from an Evaluation Standpoint

Cited by 15 publications

References 39 publications

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

The 2nd Workshop on Recommendation with Generative Models

Handling Low Homophily in Recommender Systems With Partitioned Graph Transformer

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