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With the rise of Large Language Models (LLMs) such as ChatGPT, researchers have been working on how to utilize the LLMs for better recommendations. However, although LLMs exhibit black-box and probabilistic characteristics (meaning their internal working is not visible), the evaluation framework used for assessing these LLM-based recommender systems (RS) are the same as those used for traditional recommender systems. To address this gap, we introduce the metamorphic testing for the evaluation of GPT-based RS. This testing technique involves defining of metamorphic relations (MRs) between the inputs and checking if the relationship has been satisfied in the outputs. Specifically, we examined the MRs from both RS and LLMs perspectives, including rating multiplication/shifting in RS and adding spaces/randomness in the LLMs prompt via prompt perturbation. Similarity metrics (e.g. Kendall tau and Ranking Biased Overlap(RBO)) are deployed to measure whether the relationship has been satisfied in the outputs of MRs. The experiment results on MovieLens dataset with GPT3.5 show that lower similarity are obtained in terms of Kendall $\tau$ and RBO, which concludes that there is a need of a comprehensive evaluation of the LLM-based RS in addition to the existing evaluation metrics used for traditional recommender systems.

We propose Counterfactual Analysis Quadratic Unconstrained Binary Optimization (CAQUBO) to solve QUBO problems for feature selection in recommender systems. CAQUBO leverages counterfactual analysis to measure the impact of individual features and feature combinations on model performance and employs the measurements to construct the coefficient matrix for a quantum annealer to select the optimal feature combinations for recommender systems, thereby improving their final recommendation performance. By establishing explicit connections between features and the recommendation performance, the proposed approach demonstrates superior performance compared to the state-of-the-art quantum annealing methods. Extensive experiments indicate that integrating quantum computing with counterfactual analysis holds great promise for addressing these challenges.

Using Quantum Computers to solve problems in Recommender Systems that classical computers cannot address is a worthwhile research topic. In this paper, we use Quantum Annealers to address the feature selection problem in recommendation algorithms. This feature selection problem is a Quadratic Unconstrained Binary Optimization(QUBO) problem. By incorporating Counterfactual Analysis, we significantly improve the performance of the item-based KNN recommendation algorithm compared to using pure Mutual Information. Extensive experiments have demonstrated that the use of Counterfactual Analysis holds great promise for addressing such problems., Comment: accepted by QuantumCLEF 2024

This paper investigates the Cyber-Physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators. Our analysis shows that many users exhibit a high correlation between their cyber activities and their physical context. To find this correlation, we propose a mechanism to semantically label a physical space with rich categorical information from DBPedia concepts and compute a contextual similarity that represents a user's activities with the mall context. We demonstrate the application of cyber-physical contextual similarity in two situations: user visit intent classification and future location prediction. The experimental results demonstrate that exploitation of contextual similarity significantly improves the accuracy of such applications., Comment: Accepted in ACM Transactions on Sensor Networks, 2020

Network alignment is useful for multiple applications that require increasingly large graphs to be processed. Existing research approaches this as an optimization problem or computes the similarity based on node representations. However, the process of aligning every pair of nodes between relatively large networks is time-consuming and resource-intensive. In this paper, we propose a framework, called G-CREWE (Graph CompREssion With Embedding) to solve the network alignment problem. G-CREWE uses node embeddings to align the networks on two levels of resolution, a fine resolution given by the original network and a coarse resolution given by a compressed version, to achieve an efficient and effective network alignment. The framework first extracts node features and learns the node embedding via a Graph Convolutional Network (GCN). Then, node embedding helps to guide the process of graph compression and finally improve the alignment performance. As part of G-CREWE, we also propose a new compression mechanism called MERGE (Minimum dEgRee neiGhbors comprEssion) to reduce the size of the input networks while preserving the consistency in their topological structure. Experiments on all real networks show that our method is more than twice as fast as the most competitive existing methods while maintaining high accuracy., Comment: 10 pages, accepted at the 29th ACM International Conference onInformation and Knowledge Management (CIKM 20)

Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations. To this end, we propose a translation-based relation embedding for POI recommendation. Our approach encodes the temporal and geographic information, as well as semantic contents effectively in a low-dimensional relation space by using Knowledge Graph Embedding techniques. To further alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the effectiveness of our proposed model., Comment: 12 pages, 3 figures, Accepted in the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020)

We analyze 18 million rows of Wi-Fi access logs collected over a one year period from over 120,000 anonymized users at an inner-city shopping mall. The anonymized dataset gathered from an opt-in system provides users' approximate physical location, as well as Web browsing and some search history. Such data provides a unique opportunity to analyze the interaction between people's behavior in physical retail spaces and their Web behavior, serving as a proxy to their information needs. We find: (1) the use of Wi-Fi network maps the opening hours of the mall; (2) there is a weekly periodicity in users' visits to the mall; (3) around 60% of registered Wi-Fi users actively browse the Web and around 10% of them use Wi-Fi for accessing Web search engines; (4) people are likely to spend a relatively constant amount of time browsing the Web while their visiting duration may vary; (5) people tend to visit similar mall locations and Web content during their repeated visits to the mall; (6) the physical spatial context has a small but significant influence on the Web content that indoor users browse; (7) accompanying users tend to access resources from the same Web domains.