With the growing size of data and models in Large Recommendation Models, the time required for debugging has become increasingly prohibitive, underscoring the urgent need for effective guidance in parameter configuration. The Scaling Law (SL) offers analogous guidance in the Sequential Language domain, having achieved significant success by predicting model loss when scaling model size. However, the existing guidance from SL for Sequential Recommendation (SR) remains qualitative, which is because quantitative analysis of SL on SR encounters challenges with quality measurement on redundant sequences along with loss-performance discrepancy. In response, we introduce the Performance Law (P-Law) for SR models, which predicts model performance across various settings, intending to provide a quantitative framework for guiding the parameter optimization of future models. Initially, Performance Law utilizes Real Entropy to measure data quality, aiming to remove the low-quality influence of low-entropy redundant sequences. Subsequently, Performance Law investigates a fitting decay term, which facilitated the prediction of the major loss-performance discrepancy phenomena of overfitting, ultimately achieving quantitative performance prediction. Extensive experiment on various datasets demonstrates the effectiveness of Performance Law by displaying exceptional quantitative prediction ability against the original and modified qualitative SL. Additional application experiments on optimal parameter prediction and model expansion potential prediction also demonstrated the broad applicability of the Performance Law.

There has been sustained interest from both academia and industry throughout the years due to the importance and practicability of recommendation systems. However, several recent papers have pointed out critical issues with the evaluation process in recommender systems. Likewise, this paper takes an in-depth look at a fundamental but often neglected aspect of the evaluation procedure, i.e. the datasets themselves. To do so, we adopt a systematic and comprehensive approach to understand the datasets used for implicit feedback based top-K recommendation. We start by examining recent papers from top-tier conferences to find out how different datasets have been utilised thus far. Next, we look at the characteristics of these datasets to understand their similarities and differences. Finally, we conduct an empirical study to determine whether the choice of datasets used for evaluation can influence the observations and/or conclusions obtained. Our findings suggest that greater attention needs to be paid to the selection process of datasets used for evaluating recommender systems in order to improve the robustness of the obtained results.

Recommender systems play a vital role in modern web services. In a typical recommender system, we are given a set of observed user-item interaction records and seek to uncover the hidden behavioral patterns of users from these historical interactions. By exploiting these hidden patterns, we aim to discover users’ personalized tastes and recommend them new items. Among various types of recommendation methods, the latent factor collaborative filtering models have dominated the field. In this paper, we develop a unified view for the existing latent factor models from a probabilistic perspective. The unified framework enables us to discern the underlying connections of different latent factor models and deepen our understandings of their advantages and limitations. In particular, we observe that the loss functions adopted by the existing models are oblivious to the geometry induced by the item-similarity. To address this, we propose a novel model—SinkhornCF—based on Sinkhorn divergence. To address the challenge of the expensive computational cost of Sinkhorn divergence, we also propose new techniques to enable the resulting model to be able to scale to large datasets. Its effectiveness is verified on two real-world recommendation datasets.

Tweets summarization aims to find a group of representative tweets for a specific set of input tweets or a given topic. In recent times, there have been several research efforts toward devising a variety of techniques to summarize tweets in Twitter. However, these techniques are either not personal (that is, consider only tweets in the timeline of a specific user) or are too expensive to be realized on a mobile device. Given that 80% of active Twitter users access the site on mobile devices, in this article we present a lightweight, personal, on‐demand, topic modeling‐based tweets summarization engine called TOTEM, designed for such devices. Specifically, TOTEM first preprocesses recent tweets in a user's timeline and exploits Latent Dirichlet Allocation‐based topic modeling to assign each preprocessed tweet to a topic. Then it generates a ranked list of relevant tweets, a topic label, and a topic summary for each of the topics. Our experimental study with real‐world data sets demonstrates the superiority of TOTEM.

Textual reviews, which are readily available on many e-commerce and review websites such as Amazon and Yelp, serve as an invaluable source of information for recommender systems. However, not all parts of the reviews are equally important, and the same choice of words may reflect a different meaning based on its context. In this paper, we propose a novel end-to-end Aspect-based Neural Recommender (ANR) to perform aspect-based representation learning for both users and items via an attention-based component. Furthermore, we model the multi-faceted process behind how users rate items by estimating the aspect-level user and item importance by adapting the neural co-attention mechanism. Our proposed model concurrently address several shortcomings of existing recommender systems, and a thorough experimental study on 25 benchmark datasets from Amazon and Yelp shows that ANR significantly outperforms recently proposed state-of-the-art baselines such as DeepCoNN, D-Attn and ALFM.

Tweets summarization aims to find a group of representative tweets for a specific topic. In recent times, there have been several research efforts toward devising a variety of techniques to summarize tweets in Twitter. However, these techniques are either not personal (i.e., consider only tweets in the timeline of a specific user) or are too expensive to be realized on a mobile device. Given that 80% of active Twitter users access the site on mobile devices, in this demonstration we present a lightweight, personalized, on-demand, topic modeling-based tweets summarization engine called TOTEM, designed for such devices. Specifically, TOTEM summarizes most recent tweets on a user's timeline and enables her to visualize and navigate representative topics and associated tweets in a user-friendly tap-and-swipe manner.