Cross-domain recommendation (CDR) extends conventional recommender systems by leveraging user-item interactions from dense domains to mitigate data sparsity and the cold start problem. While CDR offers substantial potential for enhancing recommendation performance, most existing CDR methods suffer from sensitivity to the ratio of overlapping users and intrinsic discrepancy between source and target domains. To overcome these limitations, in this work, we explore the application of graph signal processing (GSP) in CDR scenarios. We propose CGSP, a unified CDR framework based on GSP, which employs a cross-domain similarity graph constructed by flexibly combining target-only similarity and source-bridged similarity. By processing personalized graph signals computed for users from either the source or target domain, our framework effectively supports both inter-domain and intra-domain recommendations. Our empirical evaluation demonstrates that CGSP consistently outperforms various encoder-based CDR approaches in both intra-domain and inter-domain recommendation scenarios, especially when the ratio of overlapping users is low, highlighting its significant practical implication in real-world applications.

Recent years have witnessed a rapid growth of recommender systems, providing suggestions in numerous applications with potentially high social impact, such as health or justice. Meanwhile, in Europe, the upcoming AI Act mentions \emph{transparency} as a requirement for critical AI systems in order to ``mitigate the risks to fundamental rights''. Post-hoc explanations seamlessly align with this goal and extensive literature on the subject produced several forms of such objects, graphs being one of them. Early studies in visualization demonstrated the graphs' ability to improve user understanding, positioning them as potentially ideal explanations. However, it remains unclear how graph-based explanations compare to other explanation designs. In this work, we aim to determine the effectiveness of graph-based explanations in improving users' perception of AI-based recommendations using a mixed-methods approach. We first conduct a qualitative study to collect users' requirements for graph explanations. We then run a larger quantitative study in which we evaluate the influence of various explanation designs, including enhanced graph-based ones, on aspects such as understanding, usability and curiosity toward the AI system. We find that users perceive graph-based explanations as more usable than designs involving feature importance. However, we also reveal that textual explanations lead to higher objective understanding than graph-based designs. Most importantly, we highlight the strong contrast between participants' expressed preferences for graph design and their actual ratings using it, which are lower compared to textual design. These findings imply that meeting stakeholders' expressed preferences might not alone guarantee ``good'' explanations. Therefore, crafting hybrid designs successfully balancing social expectations with downstream performance emerges as a significant challenge.

Multimodal recommendation systems (MMRS) have received considerable attention from the research community due to their ability to jointly utilize information from user behavior and product images and text. Previous research has two main issues. First, many long-tail items in recommendation systems have limited interaction data, making it difficult to learn comprehensive and informative representations. However, past MMRS studies have overlooked this issue. Secondly, users' modality preferences are crucial to their behavior. However, previous research has primarily focused on learning item modality representations, while user modality representations have remained relatively simplistic.To address these challenges, we propose a novel Graphs and User Modalities Enhancement (GUME) for long-tail multimodal recommendation. Specifically, we first enhance the user-item graph using multimodal similarity between items. This improves the connectivity of long-tail items and helps them learn high-quality representations through graph propagation. Then, we construct two types of user modalities: explicit interaction features and extended interest features. By using the user modality enhancement strategy to maximize mutual information between these two features, we improve the generalization ability of user modality representations. Additionally, we design an alignment strategy for modality data to remove noise from both internal and external perspectives. Extensive experiments on four publicly available datasets demonstrate the effectiveness of our approach.

Continual learning(CL) is a useful technique to acquire dynamic knowledge continually. Although powerful cloud platforms can fully exert the ability of CL,e.g., customized recommendation systems, similar personalized requirements for edge devices are almost disregarded. This phenomenon stems from the huge resource overhead involved in training neural networks and overcoming the forgetting problem of CL. This paper focuses on these scenarios and proposes a compact algorithm called LightCL. Different from other CL methods bringing huge resource consumption to acquire generalizability among all tasks for delaying forgetting, LightCL compress the resource consumption of already generalized components in neural networks and uses a few extra resources to improve memory in other parts. We first propose two new metrics of learning plasticity and memory stability to seek generalizability during CL. Based on the discovery that lower and middle layers have more generalizability and deeper layers are opposite, we $\textit{Maintain Generalizability}$ by freezing the lower and middle layers. Then, we $\textit{Memorize Feature Patterns}$ to stabilize the feature extracting patterns of previous tasks to improve generalizability in deeper layers. In the experimental comparison, LightCL outperforms other SOTA methods in delaying forgetting and reduces at most $\textbf{6.16$\times$}$ memory footprint, proving the excellent performance of LightCL in efficiency. We also evaluate the efficiency of our method on an edge device, the Jetson Nano, which further proves our method's practical effectiveness.

In Reinforcement Learning-based Recommender Systems (RLRS), the complexity and dynamism of user interactions often result in high-dimensional and noisy state spaces, making it challenging to discern which aspects of the state are truly influential in driving the decision-making process. This issue is exacerbated by the evolving nature of user preferences and behaviors, requiring the recommender system to adaptively focus on the most relevant information for decision-making while preserving generaliability. To tackle this problem, we introduce an innovative causal approach for decomposing the state and extracting \textbf{C}ausal-\textbf{I}n\textbf{D}ispensable \textbf{S}tate Representations (CIDS) in RLRS. Our method concentrates on identifying the \textbf{D}irectly \textbf{A}ction-\textbf{I}nfluenced \textbf{S}tate Variables (DAIS) and \textbf{A}ction-\textbf{I}nfluence \textbf{A}ncestors (AIA), which are essential for making effective recommendations. By leveraging conditional mutual information, we develop a framework that not only discerns the causal relationships within the generative process but also isolates critical state variables from the typically dense and high-dimensional state representations. We provide theoretical evidence for the identifiability of these variables. Then, by making use of the identified causal relationship, we construct causal-indispensable state representations, enabling the training of policies over a more advantageous subset of the agent's state space. We demonstrate the efficacy of our approach through extensive experiments, showcasing our method outperforms state-of-the-art methods.

The small devices and handy accessories are the unsung heroes of the tech world. They power our tablets and laptops, lend extra storage to our cameras and handhelds, and even bring a little entertainment in the form of smaller speakers and streaming devices. Now that Amazon's Prime Day is in full swing we've rounded up the best Prime Day deals under 50 on the smaller tech we love. As with all Engadget tech deals coverage, we only highlight discounts on gear we've tested or have otherwise used and know to be worthy of your money. We cross-checked our guides and reviews with the Prime Day deals Amazon has put forth to come up with what you see here. The Anker Nano power bank in black is on sale for Prime Day for 16.13. That's a 15 percent discount and a good deal for one of the best power banks we tested. We like the foldable USB-C connector which means you don't have to remember a separate cable and the amount of charge it delivers for such a small package. Plus it's compact enough you can use it while it refills your phone.

Amazon Prime Day is a chance for Prime members to pick up all sorts of things on sale, and while most of the discounts aren't worth your time, those on gadgets actually can be. Prime Day deals have discounted plenty of our favorites in the past, and this year is shaping up to be no different. Engadget has you covered if any of the items on your wishlist happen to fall under the consumer tech umbrella. We've sifted through all of the junk to find all of the tech deals that are actually worth your time. And the good news is that not all of the discounts are on high-priced items.

Amazon's (twice) yearly Prime Day event has arrived and with it comes a slew of low prices on big ticket items like laptops and TVs -- but the savings on smaller stuff can be just as worthy, particularly if you need a the batteries, cables, chargers and more that keep your bigger accessories running. We're also seeing deals on smaller gadgets like Bluetooth trackers, speakers and streaming devices -- all of which represent the best Prime Day deals under 50. Check out the sales below for the more affordable Prime Day deals you can shop right now. As with all Engadget tech deals coverage, we only highlight discounts on gear we've tested or have otherwise used and know to be worthy of your money. We cross-checked our guides and reviews with the Prime Day deals Amazon has put forth to come up with what you see here. The smallest Echo speaker is the Echo Pop, and right now it's down to 18 instead of the full 40, which is a 55 percent discount and matches the lowest price we've seen from past sales. The smaller size means you can add Alexa's help and smart home control to dorm rooms, bathrooms, tiny kitchens or other spots without a lot of space. The discount applies to all four colorways: black, white, lavender and teal.

Amazon Prime Day is usually a great time to pick up things for your home, particularly smart home tech. This year, a bunch of Google's Nest devices have been discounted, with many down to record-low prices. These gadgets are best for anyone who already lives within the Google ecosystem, especially those who already rely on the Google Assistant to help them get things done. You'll find a few Nest security cameras on sale for Prime Day, as well as video doorbells and Wi-Fi systems. If you're looking for even more Prime Day deals, check out Engadget's Prime Day hub where you'll find all of the best tech deals you can get for the shopping event this year.

Amazon Prime Day covers deals across every department -- but at Engadget, we focus on discounts that apply to the tech we've tested and recommend. Not surprisingly, much of that gear can be pretty pricey. Cables, chargers, mini speakers and budget earbuds might not be as flashy as a laptop or a new TV, but they're arguably just as important. Also, it feels good to snag some small stuff for 40, 30 and even 20. Check out the list below for a few dozen of the best Prime Day tech deals under 50. As with all Engadget tech deals coverage, we only highlight discounts on gear we've tested or have otherwise used and know to be worthy of your money. We cross-checked our guides and reviews with the Prime Day deals Amazon has put forth to come up with what you see here. The smallest Echo speaker is the Echo Pop, and right now it's down to 18 instead of the full 40, which is a 55 percent discount and matches the lowest price we've seen from past sales.