Session 3: Applications

Date: Wednesday September 20, 2:00 PM – 3:20 PM (GMT+8)
Room: Hall 406CX
Session Chair: Alan Said
Parallel with: Session 4: Trustworthy Recommendation

• REPHUMMUS: A Linked, Healthiness-Aware, User-centered and Argument-Enabling Recipe Data Set for Recommendation
  by Felix Bölz (INSA Lyon & University of Passau), Diana Nurbakova (INSA Lyon), Sylvie Calabretto (INSA Lyon), Armin Gerl (University of Passau), Lionel Brunie (INSA Lyon) and Harald Kosch (University of Passau)

  The overweight and obesity rate is increasing for decades worldwide. Healthy nutrition is, besides education and physical activity, one of the various keys to tackle this issue. In an effort to increase the availability of digital, healthy recommendations, the scientific area of food recommendation extends its focus from the accuracy of the recommendations to beyond-accuracy goals like transparency and healthiness. To address this issue a data basis is required, which in the ideal case encompasses user-item interactions like ratings and reviews, food-related information like recipe details, nutritional data, and in the best case additional data which describes the food items and their relations semantically. Though several recipe recommendation data sets exist, to the best of our knowledge, a holistic large-scale healthiness-aware and connected data sets have not been made available yet. The lack of such data could partially explain the poor popularity of the topic of healthy food recommendation when compared to the domain of movie recommendation. In this paper, we show that taking into account only user-item interactions is not sufficient for a recommendation. To close this gap, we propose a connected data set called HUMMUS (Health-aware User-centered recoMMedation and argUment enabling data Set) collected from Food.com containing multiple features including rich nutrient information, text reviews, and ratings, enriched by the authors with extra features such as Nutri-scores and connections to semantic data like the FoodKG and the FoodOn ontology. We hope that these data will contribute to the healthy food recommendation domain.

  Full text in ACM Digital Library

• RESFast and Examination-agnostic Reciprocal Recommendation in Matching Markets
  by Yoji Tomita (CyberAgent, Inc.), Riku Togashi (CyberAgent, Inc.), Yuriko Hashizume (CyberAgent, Inc.) and Naoto Ohsaka (CyberAgent, Inc.).

  n matching markets such as job posting and online dating platforms, the recommender system plays a critical role in the success of the platform. Unlike standard recommender systems that suggest items to users, reciprocal recommender systems (RRSs) that suggest other users must take into account the mutual interests of users. In addition, ensuring that recommendation opportunities do not disproportionately favor popular users is essential for the total number of matches and for fairness among users. Existing recommendation methods in matching markets, however, face computational challenges on large-scale platforms and depend on specific examination functions in the position-based model (PBM). In this paper, we introduce the reciprocal recommendation method based on the matching with transferable utility (TU matching) model in the context of ranking recommendations in matching markets and propose a fast and examination-model-free algorithm. Furthermore, we evaluate our approach on experiments with synthetic data and real-world data from an online dating platform in Japan. Our method performs better than or as well as existing methods in terms of the number of total matches and works well even in a large-scale dataset for which one existing method does not work.

  Full text in ACM Digital Library

• RESGoing Beyond Local: Global Graph-Enhanced Personalized News Recommendations
  by Boming Yang (The University of Tokyo), Dairui Liu (University College Dublin), Toyotaro Suzumura (The University of Tokyo), Ruihai Dong (University College Dublin) and Irene Li (The University of Tokyo).

  Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent work primarily focuses on using advanced natural language processing (NLP) techniques to extract semantic information from rich textual data, employing content-based methods derived from locally viewed historical clicked news. However, this approach lacks a global perspective, failing to account for users’ hidden motivations and behaviors beyond semantic information. To address this challenge, we propose a novel model called GLORY(Global-LOcal news Recommendation sYstem), which combines global news representations learned from other users with local news representations to enhance personalized recommendation systems. We accomplish this by constructing a Global Clicked News Encoder, which includes a global news graph and employs gated graph neural networks to fuse news representations, thereby enriching clicked news representations. Similarly, we extend this approach to a Global Candidate News Encoder, utilizing a global entity graph and candidate news fusion to enhance candidate news representation. Evaluation results on two public news datasets demonstrate that our method outperforms existing approaches. Furthermore, our model offers more diverse recommendations.

  Full text in ACM Digital Library

• RESMasked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping
  by Ming Li (University of Amsterdam), Mozhdeh Ariannezhad (University of Amsterdam), Andrew Yates (University of Amsterdam) and Maarten de Rijke (University of Amsterdam).

  Next basket recommendation (NBR) is the task of predicting the next set of items based on a sequence of already purchased baskets. It is a recommendation task that has been widely studied, especially in the context of grocery shopping. In NBR, it is useful to distinguish between repeat items, i.e., items that a user has consumed before, and explore items, i.e., items that a user has not consumed before. Most NBR work either ignores this distinction or focuses on repeat items.

  We formulate the next novel basket recommendation (NNBR) task, i.e., the task of recommending a basket that only consists of novel items, which is valuable for both real-world application and NBR evaluation. We evaluate how existing NBR methods perform on the NNBR task and find that, so far, limited progress has been made w.r.t. the NNBR task. To address the NNBR task, we propose a simple bi-directional transformer basket recommendation model (BTBR), which is focused on directly modeling item-to-item correlations within and across baskets instead of learning complex basket representations. To properly train BTBR, we propose and investigate several masking strategies and training objectives: (i) item-level random masking, (ii) item-level select masking, (iii) basket-level all masking, (iv) item basket-level explore masking, and (v) joint masking. In addition, an item-basket swapping strategy is proposed to enrich the item interactions within the same baskets.

  We conduct extensive experiments on three open datasets with various characteristics. The results demonstrate the effectiveness of BTBR and our masking and swapping strategies for the NNBR task. BTBR with a properly selected masking and swapping strategy can substantially improve the NNBR performance.

  Full text in ACM Digital Library

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