Factorization machine (FM) variants are widely used in recommendation systems that operate under strict throughput and latency requirements, such as online advertising systems. FMs are known both due to their ability to model pairwise feature interactions while being resilient to data sparsity, and their computational graphs that facilitate fast inference and training. Moreover, when items are ranked as a part of a query for each incoming user, these graphs facilitate computing the portion stemming from the user and context fields only once per query. Consequently, in terms of inference cost, the number of user or context fields is practically unlimited. More advanced FM variants, such as FwFM, provide better accuracy by learning a representation of field-wise interactions, but require computing all pairwise interaction terms explicitly. The computational cost during inference is proportional to the square of the number of fields, including user, context, and item. When the number of fields is large, this is prohibitive in systems with strict latency constraints. To mitigate this caveat, heuristic pruning of low intensity field interactions is commonly used to accelerate inference. In this work we propose an alternative to the pruning heuristic in FwFMs using a diagonal plus symmetric low-rank decomposition. Our technique reduces the computational cost of inference, by allowing it to be proportional to the number of item fields only. Using a set of experiments on real-world datasets, we show that aggressive rank reduction outperforms similarly aggressive pruning, both in terms of accuracy and item recommendation speed. We corroborate our claim of faster inference experimentally, both via a synthetic test, and by having deployed our solution to a major online advertising system. The code to reproduce our experimental results is at https://github.com/michaelviderman/pytorch-fm/tree/dev.

One thing seems for sure: people are not ready for "digital workers" just yet. That's the lesson learned by Sarah Franklin, the CEO of Lattice, a human resources and performance management platform that offers performance coaching, talent reviews, onboarding automation, compensation management and a host of other HR tools to more than 5,000 organizations around the world. What is a digital employee? According to Franklin, it's avatars like Devin the engineer, Harvey the lawyer, Einstein the service agent and Piper the sales agent who have "entered the workforce and become our colleagues". But these are not real workers.

But the days of deliberating whether or not to include your friends, dogs, or selfies in your dating app profile could soon be a thing of the past, thanks to Tinder. The dating app has released a new tool called'Photo Selector', which uses AI to choose the best photos for your dating profile. 'By alleviating the burden of photo selection, Photo Selector empowers users to focus more on making meaningful connections rather than spending excessive time on photo selection,' Tinder explained. 'This AI innovation promises to inject more spontaneity into the online dating experience.' Tinder has released a new tool called'Photo Selector', which uses AI to choose the best photos for your dating profile To use the tool, open the Tinder app and select'Add media' on your profile. First, you'll be prompted to upload a photo of yourself, or take a selfie.

User interests can be viewed over different time scales, mainly including stable long-term preferences and changing short-term intentions, and their combination facilitates the comprehensive sequential recommendation. However, existing work that focuses on different time scales of user modeling has ignored the negative effects of different time-scale noise, which hinders capturing actual user interests and cannot be resolved by conventional sequential denoising methods. In this paper, we propose a Long- and Short-term Interest Denoising Network (LSIDN), which employs different encoders and tailored denoising strategies to extract long- and short-term interests, respectively, achieving both comprehensive and robust user modeling. Specifically, we employ a session-level interest extraction and evolution strategy to avoid introducing inter-session behavioral noise into long-term interest modeling; we also adopt contrastive learning equipped with a homogeneous exchanging augmentation to alleviate the impact of unintentional behavioral noise on short-term interest modeling. Results of experiments on two public datasets show that LSIDN consistently outperforms state-of-the-art models and achieves significant robustness.

Recommender systems present relevant contents to users and help content creators reach their target audience. The dual nature of these systems influences both users and creators: users' preferences are affected by the items they are recommended, while creators are incentivized to alter their contents such that it is recommended more frequently. We define a model, called user-creator feature dynamics, to capture the dual influences of recommender systems. We prove that a recommender system with dual influence is guaranteed to polarize, causing diversity loss in the system. We then investigate, both theoretically and empirically, approaches for mitigating polarization and promoting diversity in recommender systems. Unexpectedly, we find that common diversity-promoting approaches do not work in the presence of dual influence, while relevancy-optimizing methods like top-$k$ recommendation can prevent polarization and improve diversity of the system.

Graph neural networks (GNNs) have recently emerged as an effective approach to model neighborhood signals in collaborative filtering. Towards this research line, graph contrastive learning (GCL) demonstrates robust capabilities to address the supervision label shortage issue through generating massive self-supervised signals. Despite its effectiveness, GCL for recommendation suffers seriously from two main challenges: i) GCL relies on graph augmentation to generate semantically different views for contrasting, which could potentially disrupt key information and introduce unwanted noise; ii) current works for GCL primarily focus on contrasting representations using sophisticated networks architecture (usually deep) to capture high-order interactions, which leads to increased computational complexity and suboptimal training efficiency. To this end, we propose L2CL, a principled Layer-to-Layer Contrastive Learning framework that contrasts representations from different layers. By aligning the semantic similarities between different layers, L2CL enables the learning of complex structural relationships and gets rid of the noise perturbation in stochastic data augmentation. Surprisingly, we find that L2CL, using only one-hop contrastive learning paradigm, is able to capture intrinsic semantic structures and improve the quality of node representation, leading to a simple yet effective architecture. We also provide theoretical guarantees for L2CL in minimizing task-irrelevant information. Extensive experiments on five real-world datasets demonstrate the superiority of our model over various state-of-the-art collaborative filtering methods. Our code is available at https://github.com/downeykking/L2CL.

If you ever feel like Gen Z is speaking an entirely different language, you are definitely not alone. Luckily for all the baffled parents out there, a new Alexa update should help you avoid giving your kids the ick with your sus Gen Z slang. From today, Amazon users will be able to ask Alexa to'talk Gen Z to me' to reveal one of 20 phrases using Gen Z's favourite words. That might include phrases such as'the math isn't mathing' which Alexa defines as'something is incorrect or unreasonable, something doesn't add up or make sense'. And as Amazon's survey of 2,385 Britons reveals that 83 per cent of Gen Z say the older generation fails to understand them, a little extra assistance might be welcome.

Prime Day is officially over. Did your friend mention a killer deal they scored? Are you now dealing with FOMO? Well not to worry, roughly half of the Amazon Prime Day deals we highlighted during the main event are still kicking around, though they are expiring quickly. These are all products we here at WIRED have tested and recommend--some prices have slightly increased but are still a sale price, while a few have gone lower. Your next opportunity to score a good deal is around October and November, for Amazon's second Prime Day sale event and Black Friday, so take advantage, but only buy something if you actually want or need it. We test products year-round and handpicked these Prime Day deals. Products that are sold out or no longer discounted will be crossed out. We'll update this guide regularly throughout Prime Day by adding fresh deals and removing dead deals. If you buy something using links in our stories, we may earn a commission. This helps support our journalism.

Amazon Prime Day is officially over -- but there are still a few legacy deals hanging on. We had previously pulled together this list of worthy under- 25 deals, and we've just updated it to reflect these affordable bargains that are still available as of Thursday morning. As a reminder (and for the uninitiated): Engadget treats tech deals with the same care as we would "regular" tech news. When we scour the web for deals, we're looking not only for the best prices possible, but also the best products as well. Our goal with our deals coverage, especially surrounding events like Amazon Prime Day, is to surface only the best deals we can find on the gadgets we've tested and rated highly, or that we've used and know to be worth your money.

Despite its breakthrough in classification problems, Knowledge distillation (KD) to recommendation models and ranking problems has not been studied well in the previous literature. This dissertation is devoted to developing knowledge distillation methods for recommender systems to fully improve the performance of a compact model. We propose novel distillation methods designed for recommender systems. The proposed methods are categorized according to their knowledge sources as follows: (1) Latent knowledge: we propose two methods that transfer latent knowledge of user/item representation. They effectively transfer knowledge of niche tastes with a balanced distillation strategy that prevents the KD process from being biased towards a small number of large preference groups. Also, we propose a new method that transfers user/item relations in the representation space. The proposed method selectively transfers essential relations considering the limited capacity of the compact model. (2) Ranking knowledge: we propose three methods that transfer ranking knowledge from the recommendation results. They formulate the KD process as a ranking matching problem and transfer the knowledge via a listwise learning strategy. Further, we present a new learning framework that compresses the ranking knowledge of heterogeneous recommendation models. The proposed framework is developed to ease the computational burdens of model ensemble which is a dominant solution for many recommendation applications. We validate the benefit of our proposed methods and frameworks through extensive experiments. To summarize, this dissertation sheds light on knowledge distillation approaches for a better accuracy-efficiency trade-off of the recommendation models.