On Cross-Domain Transfer in Venue Recommendation

Manotumruksa, Jarana; Rafailidis, Dimitrios; Macdonald, Craig; Ounis, Iadh

doi:10.1007/978-3-030-15712-8_29

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…[39] extends meta-learning to enhance recommendations in a POI setting, but is limited to a specific region. Information transfer across graphs is not a trivial task [32,34,71] and recent mobility models that incorporate graphs with meta-learning in [46,51] are either limited to traffic datasets and do not incorporate the social network or are limited to new trajectories [16,27]. From our experiments, we prove that a mere fine-tuning on the target data is susceptible to large cross-data variances and thus re-calibrating a generative model is not a trivial task in mobility-based networks.…”

Section: Graph Based Recommendationmentioning

confidence: 99%

Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer

Gupta

Bedathur

2022

ACM Trans. Intell. Syst. Technol.

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Variability in social app usage across regions results in a high skew of the quantity and the quality of check-in data collected, which in turn is a challenge for effective location recommender systems. In this paper, we present Axolotl (Automated cross Location-network Transfer Learning), a novel method aimed at transferring location preference models learned in a data-rich region to significantly boost the quality of recommendations in a data-scarce region. Axolotl predominantly deploys two channels for information transfer, (1) a meta-learning based procedure learned using location recommendation as well as social predictions, and (2) a lightweight unsupervised cluster-based transfer across users and locations with similar preferences. Both of these work together synergistically to achieve improved accuracy of recommendations in data-scarce regions without any prerequisite of overlapping users and with minimal fine-tuning. We build Axolotl on top of a twin graph-attention neural network model used for capturing the user- and location-conditioned influences in a user-mobility graph for each region. We conduct extensive experiments on 12 user mobility datasets across the U.S., Japan, and Germany, using 3 as source regions and 9 of them (that have much sparsely recorded mobility data) as target regions. Empirically, we show that Axolotl achieves up to 18% better recommendation performance than the existing state-of-the-art methods across all metrics.

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Section: Graph Based Recommendationmentioning

confidence: 99%

Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer

Gupta

Bedathur

2022

ACM Trans. Intell. Syst. Technol.

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“…While virtual assistants are designed to complete specific tasks in users' daily life, the goal of recommendation systems is also to transfer the knowledge of users across different domains/tasks, also known as cross-domain recommendation systems. The challenge in cross-domain recommendation systems is to capture users' multi-aspect behaviours when transferring knowledge and generating recommendations for various domains [52]- [56]. Adjustment to different domains based on users' preferences is a key factor that is currently missing from virtual assistants which focus only on a specific domain.…”

Section: Adjustment To Cross-domain Recommendation Tasksmentioning

confidence: 99%

The Technological Gap Between Virtual Assistants and Recommendation Systems

Rafailidis¹,

Manolopoulos²

2019

Preprint

Self Cite

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Virtual assistants, also known as intelligent conversational systems such as Google's Virtual Assistant and Apple's Siri, interact with human-like responses to users' queries and finish specific tasks. Meanwhile, existing recommendation technologies model users' evolving, diverse and multi-aspect preferences to generate recommendations in various domains/applications, aiming to improve the citizens' daily life by making suggestions. The repertoire of actions is no longer limited to the one-shot presentation of recommendation lists, which can be insufficient when the goal is to offer decision support for the user, by quickly adapting to his/her preferences through conversations. Such an interactive mechanism is currently missing from recommendation systems. This article sheds light on the gap between virtual assistants and recommendation systems in terms of different technological aspects. In particular, we try to answer the most fundamental research question, which are the missing technological factors to implement a personalized intelligent conversational agent for producing accurate recommendations while taking into account how users behave under different conditions. The goal is, instead of adapting humans to machines, to actually provide users with better recommendation services so that machines will be adapted to humans in daily life.

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“…Actually, improvements identiied in shared-nothing cross-domain recommendation have been shown to simply be due to increased model capacity[30].…”

mentioning

confidence: 99%

Contrastive Graph Prompt-tuning for Cross-domain Recommendation

Yi,

Ounis,

MacDonald

2023

ACM Trans. Inf. Syst.

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Recommender systems commonly suffer from the long-standing data sparsity problem where insufficient user-item interaction data limits the systems’ ability to make accurate recommendations. This problem can be alleviated using cross-domain recommendation techniques. In particular, in a cross-domain setting, knowledge sharing between domains permits improved effectiveness on the target domain. While recent cross-domain recommendation techniques used a pre-training configuration, we argue that such techniques lead to a low fine-tuning efficiency, especially when using large neural models. In recent language models, prompts have been used for parameter-efficient and time-efficient tuning of the models on the downstream tasks - these prompts represent a tunable latent vector that permits to freeze the rest of the language model’s parameters. To address the cross-domain recommendation task in an efficient manner, we propose a novel Personalised Graph Prompt-based Recommendation (PGPRec) framework, which leverages the efficiency benefits from prompt-tuning. In such a framework, we develop personalised and item-wise graph prompts based on relevant items to those items the user has interacted with. In particular, we apply Contrastive Learning (CL) to generate the pre-trained embeddings, to allow an increased generalisability in the pre-training stage and to ensure an effective prompt-tuning stage. To evaluate the effectiveness of our PGPRec framework in a cross-domain setting, we conduct an extensive evaluation with the top- k recommendation task and perform a cold-start analysis. The obtained empirical results on four Amazon Review datasets show that our proposed PGPRec framework can reduce up to 74% of the tuned parameters with a competitive performance and achieves an 11.41% improved performance compared to the strongest baseline in a cold-start scenario.

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On Cross-Domain Transfer in Venue Recommendation

Cited by 9 publications

References 23 publications

Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer

Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer

The Technological Gap Between Virtual Assistants and Recommendation Systems

Contrastive Graph Prompt-tuning for Cross-domain Recommendation

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