A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists. The problem thus is sharply different from the more traditional items-to-users recommendation, since a good match requires meeting the preferences of both users. We initiate a rigorous theoretical investigation of the reciprocal recommendation task in a specific framework of sequential learning. We point out general limitations, formulate reasonable assumptions enabling effective learning and, under these assumptions, we design and analyze a computationally efficient algorithm that uncovers mutual likes at a pace comparable to those achieved by a clairvoyant algorithm knowing all user preferences in advance. Finally, we validate our algorithm against synthetic and real-world datasets, showing improved empirical performance over simple baselines.

Implicit feedback is widely used in collaborative filtering methods for recommendation. It is well known that implicit feedback contains a large number of values that are \emph{missing not at random} (MNAR); and the missing data is a mixture of negative and unknown feedback, making it difficult to learn user's negative preferences. Recent studies modeled \emph{exposure}, a latent missingness variable which indicates whether an item is missing to a user, to give each missing entry a confidence of being negative feedback. However, these studies use static models and ignore the information in temporal dependencies among items, which seems to be a essential underlying factor to subsequent missingness. To model and exploit the dynamics of missingness, we propose a latent variable named ``\emph{user intent}'' to govern the temporal changes of item missingness, and a hidden Markov model to represent such a process. The resulting framework captures the dynamic item missingness and incorporate it into matrix factorization (MF) for recommendation. We also explore two types of constraints to achieve a more compact and interpretable representation of \emph{user intents}. Experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommender systems.

A common assumption in recommender systems (RS) is the existence of a best fixed recommendation strategy. Such strategy may be simple and work at the item level (e.g., in multi-armed bandit it is assumed one best fixed arm/item exists) or implement more sophisticated RS (e.g., the objective of A/B testing is to find the best fixed RS and execute it thereafter). We argue that this assumption is rarely verified in practice, as the recommendation process itself may impact the user's preferences. For instance, a user may get bored by a strategy, while she may gain interest again, if enough time passed since the last time that strategy was used. In this case, a better approach consists in alternating different solutions at the right frequency to fully exploit their potential. In this paper, we first cast the problem as a Markov decision process, where the rewards are a linear function of the recent history of actions, and we show that a policy considering the long-term influence of the recommendations may outperform both fixed-action and contextual greedy policies. We then introduce an extension of the UCRL algorithm ( L IN UCRL) to effectively balance exploration and exploitation in an unknown environment, and we derive a regret bound that is independent of the number of states.

Conniving couple whose greedy'pervert' plot'drove innocent disabled man to suicide' given stunningly short sentences Obama Center asks for 100 unpaid volunteers despite hiring the former president's'close friend' as CEO on $740K Harry Styles shares gay kiss with SNL star in wild opening monologue as he addresses'queerbaiting' claims Housing nightmare in America's'best state to buy a home' as banks suddenly seize thousands of properties Insufferable blowhard Stephen Colbert is being taken out like the trash... and thank God! What he's done is so diabolical: MAUREEN CALLAHAN JFK Jr's mortifying night of phone sex... day Sarah Jessica Parker ditched her underwear to seduce him in public... and the girlfriend he REALLY wanted to marry: All the women before Carolyn Mass cancellations as Southwest Airlines pulls out of two of America's biggest airports Truth about'super secretive' Michael B. Jordan's love life... and real reason he is perpetually single: Years of private'heartache' and'loneliness' laid bare Beloved young dad and inspiring female'Air Force superstar' among US heroes killed in Iran mission crash as all six are named Insane moment NYC cab plows into pedestrians... and the miracle that saved them from death Caitlin Clark goes viral for bizarre behavior after Team USA's win over Italy: 'What are you doing? We fled Trump to chase the REAL American dream in the most idyllic European hotspot... here's why we're coming back to a red state I looked like a monster after a car accident burned off my face... but a pioneering face transplant gave me my life back. Furious flower farm owner blasts'feral' customers after they trampled tulips to get perfect photos Harry and Meghan hit back at new book claims she was accused by Camilla of'brainwashing' him - dismissing accusations as'deranged conspiracy' Extramarital sex with witches, cursed bloodlines and possessed politicians: DC's chief exorcist reveals the potent stench of evil among America's elite Iran's deadly drone arsenal is a'wake-up call for America': Expert warns US defenses may be unprepared for swarm attacks Iran's foreign minister admits Islamic Republic is receiving military support from Russia and China Hollywood costume designer names VILE A-Listers including pervert James Bond star, slob female sitcom icon... and details the hilarious evil of Shannen Doherty Scientists reveal exactly where you're going WRONG on your dating profile - and the simple changes you can make to bag a date Are you struggling to find love online? Scientists have revealed where you're probably going wrong. A new study has uncovered nine different types of profile photos that singletons tend to use - and what they convey about you to fellow daters.

The company hopes to use its Bee chatbot to connect compatible users without the need for swipes. Bumble is testing an AI dating assistant called Bee that it hopes will get users on dates without them having to swipe through profiles, writes . The company announced the AI assistant during its fourth quarter earnings, and intends to use the AI in a new experience it calls Dates. When a user opts in to Bumble's Dates feature, Bee performs an onboarding chat where it learns about the users' values, relationship goals, communications style, lifestyle and dating intentions, and then attempts to find other users who share some or all of those traits. Once Bee finds someone compatible, both users are notified in the app that they could be a great match, and receive a summary generated by Bee explaining why.

Samsung Galaxy Unpacked 2026 is Feb. 25 It's now out in select markets with One UI 8.5 beta, including the US. Bixby isn't typically part of the conversation when it comes to virtual assistants for mobile devices, but Samsung is clearly hoping that you would use it more. The company has launched the latest version of Bixby with the new One UI 8.5 beta, and it has been tweaked to work as a "conversational agent." Samsung says you'll now be able to talk to it and give it tasks using natural language, like how you'd talk to other people or, these days, to chatbots. You don't have to remember exact commands or names for specific settings.

Intel is launching'Ask Intel,' an AI virtual assistant built on Microsoft Copilot Studio to handle customer support cases and warranty checks. PCWorld reports this shift follows Intel's removal of inbound phone support in December, directing customers to online assistance instead. The AI system warns users its answers may be inaccurate, raising concerns about potential hardware damage from incorrect technical advice. If your Intel processor requires a warranty return or support, the first "person" you'll probably be dealing with at Intel will be an AI. Intel is rolling out "Ask Intel," an addition to its Intel support site, that runs on Microsoft Copilot rather than on human intervention. Ask Intel will appear as part of support.intel.com

The collected data in recommender systems generally suffers selection bias. Considerable works are proposed to address selection bias induced by observed user and item features, but they fail when hidden features (e.g., user age or salary) that affect

The dual nature of these systems naturally influences both users and creators: users' preferences are