In this technical survey, we comprehensively summarize the latest advancements in the field of recommender systems. The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems. The study starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems, and then delves into the category of knowledge-based recommender systems. In addition, the survey analyzes the robustness, data bias, and fairness issues in recommender systems, summarizing the evaluation metrics used to assess the performance of these systems. Finally, the study provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field.

Recommender systems are designed to predict user preferences over collections of items. These systems process users' previous interactions to decide which items should be ranked higher to satisfy their desires. An ensemble recommender system can achieve great recommendation performance by effectively combining the decisions generated by individual models. In this paper, we propose a novel ensemble recommender system that combines predictions made by different models into a unified hypergraph ranking framework. This is the first time that hypergraph ranking has been employed to model an ensemble of recommender systems. Hypergraphs are generalizations of graphs where multiple vertices can be connected via hyperedges, efficiently modeling high-order relations. We differentiate real and predicted connections between users and items by assigning different hyperedge weights to individual recommender systems. We perform experiments using four datasets from the fields of movie, music and news media recommendation. The obtained results show that the ensemble hypergraph ranking method generates more accurate recommendations compared to the individual models and a weighted hybrid approach. The assignment of different hyperedge weights to the ensemble hypergraph further improves the performance compared to a setting with identical hyperedge weights.

'The Five' co-hosts discuss Rep. Cori Bush, D-Mo., arguing at a congressional hearing that being anti-woke is being anti-Black. Hollywood and movie critics have become "predictable" in pursuing a divisive political agenda in films, a conservative film reviewer says, leaving audiences alienated and frustrated. James Carrick, founder of "Worth it or Woke?", told Fox News Digital he decided to create a movie review site that is transparent about its conservative Christian perspective, as an alternative to long-established film rating sites like "Rotten Tomatoes." The site launched in February and has reviews from current films in theaters, streaming series and favorite films that came out years ago which Carrick believes are "worth it" for audiences to see. A film enthusiast with a background in theater and philosophy, Carrick said he hopes his site will help others "vote with their dollars" to avoiding films with woke messages if they choose.

The United States Federal Trade Commission has accused Amazon.com of enrolling millions of consumers into its paid subscription Amazon Prime service without their consent and making it hard for them to cancel, the latest action by the agency against the e-commerce giant in recent weeks. The FTC sued in Amazon in federal court in Seattle on Wednesday, alleging that "Amazon has knowingly duped millions of consumers into unknowingly enrolling in Amazon Prime." The FTC said Amazon used "manipulative, coercive or deceptive user-interface designs known as'dark patterns' to trick consumers into enrolling in automatically renewing Prime subscriptions." The lawsuit is one of several actions taken by President Joe Biden's administration intended to rein in the outsized market power of Big Tech firms as it tries to increase competition to create greater consumer protection. The FTC said Amazon Prime is the world's largest subscription programme, generating $25bn in revenue annually.

Kurt "The CyberGuy" Knutsson explains five great accessibility features on your Apple iPhone. If you have an iPhone, you probably know about the helpful function of "Siri," or your Apple virtual assistant. Depending on how you set up your phone, if you hold down the side button (or home button if your model has one) or say "Hey Siri," you'll easily activate the feature. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S TO MAKE YOU SMARTER You may use Siri to call friends or family, send texts, and maybe you occasionally ask a question. However, did you know about these 10 super-helpful features?

Sequential recommendation (SR) investigates the dynamic user preferences modeling and generates the next-item prediction. The next item preference is typically generated by the affinity between the sequence and item representations. However, both sequence and item representations suffer from the rank degeneration issue due to the data sparsity problem. The rank degeneration issue significantly impairs the representations for SR. This motivates us to measure how severe is the rank degeneration issue and alleviate the sequence and item representation rank degeneration issues simultaneously for SR. In this work, we theoretically connect the sequence representation degeneration issue with the item rank degeneration, particularly for short sequences and cold items. We also identify the connection between the fast singular value decay phenomenon and the rank collapse issue in transformer sequence output and item embeddings. We propose the area under the singular value curve metric to evaluate the severity of the singular value decay phenomenon and use it as an indicator of rank degeneration. We further introduce a novel singular spectrum smoothing regularization to alleviate the rank degeneration on both sequence and item sides, which is the Singular sPectrum sMoothing for sequential Recommendation (SPMRec). We also establish a correlation between the ranks of sequence and item embeddings and the rank of the user-item preference prediction matrix, which can affect recommendation diversity. We conduct experiments on four benchmark datasets to demonstrate the superiority of SPMRec over the state-of-the-art recommendation methods, especially in short sequences. The experiments also demonstrate a strong connection between our proposed singular spectrum smoothing and recommendation diversity.

In this study, we partition users by rating disposition - looking first at their percentage of negative ratings, and then at the general use of the rating scale. We hypothesize that users with different rating dispositions may use the recommender system differently and therefore the agreement with their past ratings may be less predictive of the future agreement. We use data from a large movie rating website to explore whether users should be grouped by disposition, focusing on identifying their various rating distributions that may hurt recommender effectiveness. We find that such partitioning not only improves computational efficiency but also improves top-k performance and predictive accuracy. Though such effects are largest for the user-based KNN CF, smaller for item-based KNN CF, and smallest for latent factor algorithms such as SVD.

Conversational AI systems such as Alexa need to understand defective queries to ensure robust conversational understanding and reduce user friction. These defective queries often arise from user ambiguities, mistakes, or errors in automatic speech recognition (ASR) and natural language understanding (NLU). Personalized query rewriting is an approach that focuses on reducing defects in queries by taking into account the user's individual behavior and preferences. It typically relies on an index of past successful user interactions with the conversational AI. However, unseen interactions within the user's history present additional challenges for personalized query rewriting. This paper presents our "Collaborative Query Rewriting" approach, which specifically addresses the task of rewriting new user interactions that have not been previously observed in the user's history. This approach builds a "User Feedback Interaction Graph" (FIG) of historical user-entity interactions and leverages multi-hop graph traversal to enrich each user's index to cover future unseen defective queries. The enriched user index is called a Collaborative User Index and contains hundreds of additional entries. To counteract precision degradation from the enlarged index, we add additional transformer layers to the L1 retrieval model and incorporate graph-based and guardrail features into the L2 ranking model. Since the user index can be pre-computed, we further investigate the utilization of a Large Language Model (LLM) to enhance the FIG for user-entity link prediction in the Video/Music domains. Specifically, this paper investigates the Dolly-V2 7B model. We found that the user index augmented by the fine-tuned Dolly-V2 generation significantly enhanced the coverage of future unseen user interactions, thereby boosting QR performance on unseen queries compared with the graph traversal only approach.

While dating apps like Tinder, Bumble and Hinge might remain the largest pools for wholesale, bulk swiping, there's a long tail of niche options for daters who already know exactly what they're looking for. So, whether you can't live without a partner who loves death metal or desperately need to marry a fellow millionaire, there's a dating app or site out there catering to your own, very specific community of singles. Here are ten of the most unusual dating services online right now. A dinner date with an attractive stranger can be stressful enough without having to consider life-threatening dietary restrictions. Enter ' Singles with Food Allergies,' a $14.95-per-month subscription dating site for finding a soulmate who shares your same food allergy A dinner date with an attractive stranger can be stressful enough without having to consider anyone's life-threatening dietary restrictions.

Congestion is a common failure mode of markets, where consumers compete inefficiently on the same subset of goods (e.g., chasing the same small set of properties on a vacation rental platform). The typical economic story is that prices solve this problem by balancing supply and demand in order to decongest the market. But in modern online marketplaces, prices are typically set in a decentralized way by sellers, with the power of a platform limited to controlling representations -- the information made available about products. This motivates the present study of decongestion by representation, where a platform uses this power to learn representations that improve social welfare by reducing congestion. The technical challenge is twofold: relying only on revealed preferences from users' past choices, rather than true valuations; and working with representations that determine which features to reveal and are inherently combinatorial. We tackle both by proposing a differentiable proxy of welfare that can be trained end-to-end on consumer choice data. We provide theory giving sufficient conditions for when decongestion promotes welfare, and present experiments on both synthetic and real data shedding light on our setting and approach.