Amazon, Netflix, and Indeed don't simply provide more options than traditional retail stores, video rental stores, and newspapers -- they provide so many more options that the human mind can effectively comprehend and parse. Users need to be shown what will most appeal to them. There were recommender systems before deep learning, but until that advancement, technical constraints ensured choice remained tyrannical. Deep learning has become an essential component of recommender systems, and anyone who wants to understand the latter must understand the former. Traditional recommender systems make recommendations to users based on previous user interactions or attributes, depending on whether the recommender system uses content-based filtering, collaborative filtering, or a hybrid of the two. Content-based filtering recommends items with similar features to items a user interacted with in the past.

Match Group, which operates one of the world's largest portfolios of dating apps, will soon add a new profile verification feature to its popular dating app Hinge. The feature is part of a larger effort to crack down on scammers who use fake photos and purport to be people they're not on the app, often with the intent of eventually scheming romantic conquests out of money. Jarryd Boyd, director of brand communications for Hinge, said in a written statement that Hinge will begin rolling out the feature, named Selfie Verification, next month. Hinge will ask users to take a video selfie within the app in order to confirm they're a real person and not a digital fake. Match Group then plans to use a combination of machine learning technology and human moderators to "compare facial geometries from the video selfie to photos on the user's profile," Boyd said.

Earlier this year, Zoom announced a Byzantine policy change that, if I thought about it at all when it happened, I probably would have expected to have almost no impact on my life: One-on-one video calls, which had previously been free and unrestricted for all non-paying users of its platform, would now have a 40-minute time limit just like group calls. A bummer for thrifty Zoom power users, perhaps, but at the time, I was blessed to live an existence of only sporadic Zooming. Then a few months ago I had occasion to start using Zoom a little more frequently. I would love to leave the reasons for this sudden Zoomassaince vague and retain one emotional support shred of dignity, but there's no real way to explain the rest without disclosing the following: I had decided it was time to "get back out there" and was using Zoom to go on video dates. After meeting and chatting with people on dating apps, I would suggest we talk on video before actually getting together in person, et violà: video date.

Recommender system has been deployed in a large amount of real-world applications, profoundly influencing people's daily life and production.Traditional recommender models mostly collect as comprehensive as possible user behaviors for accurate preference estimation. However, considering the privacy, preference shaping and other issues, the users may not want to disclose all their behaviors for training the model. In this paper, we study a novel recommendation paradigm, where the users are allowed to indicate their "willingness" on disclosing different behaviors, and the models are optimized by trading-off the recommendation quality as well as the violation of the user "willingness". More specifically, we formulate the recommendation problem as a multiplayer game, where the action is a selection vector representing whether the items are involved into the model training. For efficiently solving this game, we design a tailored algorithm based on influence function to lower the time cost for recommendation quality exploration, and also extend it with multiple anchor selection vectors.We conduct extensive experiments to demonstrate the effectiveness of our model on balancing the recommendation quality and user disclosing willingness.

As service-oriented architecture becoming one of the most prevalent techniques to rapidly deliver functionalities to customers, increasingly more reusable software components have been published online in forms of web services. To create a mashup, it gets not only time-consuming but also error-prone for developers to find suitable services from such a sea of services. Service discovery and recommendation has thus attracted significant momentum in both academia and industry. This paper proposes a novel incremental recommend-as-you-go approach to recommending next potential service based on the context of a mashup under construction, considering services that have been selected to the current step as well as its mashup goal. The core technique is an algorithm of learning the embedding of services, which learns their past goal-driven context-aware decision making behaviors in addition to their semantic descriptions and co-occurrence history. A goal exclusionary negative sampling mechanism tailored for mashup development is also developed to improve training performance. Extensive experiments on a real-world dataset demonstrate the effectiveness of our approach.

Recommender systems are often susceptible to well-crafted fake profiles, leading to biased recommendations. The wide application of recommender systems makes studying the defense against attack necessary. Among existing defense methods, data-processing-based methods inevitably exclude normal samples, while model-based methods struggle to enjoy both generalization and robustness. Considering the above limitations, we suggest integrating data processing and robust model and propose a general framework, Triple Cooperative Defense (TCD), which cooperates to improve model robustness through the co-training of three models. Specifically, in each round of training, we sequentially use the high-confidence prediction ratings (consistent ratings) of any two models as auxiliary training data for the remaining model, and the three models cooperatively improve recommendation robustness. Notably, TCD adds pseudo label data instead of deleting abnormal data, which avoids the cleaning of normal data, and the cooperative training of the three models is also beneficial to model generalization. Through extensive experiments with five poisoning attacks on three real-world datasets, the results show that the robustness improvement of TCD significantly outperforms baselines. It is worth mentioning that TCD is also beneficial for model generalizations.

When an Agent visits a platform recommending a menu of content to select from, their choice of item depends not only on fixed preferences, but also on their prior engagements with the platform. The Recommender's primary objective is typically to encourage content consumption which optimizes some reward, such as ad revenue, but they often also aim to ensure that a wide variety of content is consumed by the Agent over time. We formalize this problem as an adversarial bandit task. At each step, the Recommender presents a menu of $k$ (out of $n$) items to the Agent, who selects one item in the menu according to their unknown preference model, which maps their history of past items to relative selection probabilities. The Recommender then observes the Agent's chosen item and receives bandit feedback of the item's reward. In addition to optimizing reward from selected items, the Recommender must also ensure that the total distribution of chosen items has sufficiently high entropy. We define a class of preference models which are locally learnable, i.e. behavior over the entire domain can be estimated by only observing behavior in a small region; this includes models representable by bounded-degree polynomials as well as functions with a sparse Fourier basis. For this class, we give an algorithm for the Recommender which obtains $\tilde{O}(T^{3/4})$ regret against all item distributions satisfying two conditions: they are sufficiently diversified, and they are instantaneously realizable at any history by some distribution over menus. We show that these conditions are closely connected: all sufficiently high-entropy distributions are instantaneously realizable at any item history. We also give a set of negative results justifying our assumptions, in the form of a runtime lower bound for non-local learning and linear regret lower bounds for alternate benchmarks.

Oracle on Wednesday said that it is opening up its ERP applications platform to customer developers and partners, unveiled new B2B commerce services, and announced a variety of additions to its enterprise planning management (EPM), supply chain management (SCM) and human capital management (HCM) Fusion Cloud offerings. The updates, which were announced at the company's ongoing CloudWorld 2022 conference, are meant to not only to enhance its ERP offerings for customers, but also compete with rivals such as Microsoft, SAP, Infor and IFS. These announcements come at a time when competition in the ERP market is heating up. By 2024, at least 50% of existing customers of large ERP vendors will evaluate multiple vendors, rather than automatically adopt the latest version of their incumbent ERP suite, according to a Gartner report. Oracle's Fusion Cloud service, the market research firm noted, is targeted toward upper-midsize and large enterprises.

Multi-armed bandits (MAB) provide a principled online learning approach to attain the balance between exploration and exploitation. Due to the superior performance and low feedback learning without the learning to act in multiple situations, Multi-armed Bandits drawing widespread attention in applications ranging such as recommender systems. Likewise, within the recommender system, collaborative filtering (CF) is arguably the earliest and most influential method in the recommender system. Crucially, new users and an ever-changing pool of recommended items are the challenges that recommender systems need to address. For collaborative filtering, the classical method is training the model offline, then perform the online testing, but this approach can no longer handle the dynamic changes in user preferences which is the so-called cold start. So how to effectively recommend items to users in the absence of effective information? To address the aforementioned problems, a multi-armed bandit based collaborative filtering recommender system has been proposed, named BanditMF. BanditMF is designed to address two challenges in the multi-armed bandits algorithm and collaborative filtering: (1) how to solve the cold start problem for collaborative filtering under the condition of scarcity of valid information, (2) how to solve the sub-optimal problem of bandit algorithms in strong social relations domains caused by independently estimating unknown parameters associated with each user and ignoring correlations between users.

Recommender systems are fundamental information filtering techniques to recommend content or items that meet users' personalities and potential needs. As a crucial solution to address the difficulty of user identification and unavailability of historical information, session-based recommender systems provide recommendation services that only rely on users' behaviors in the current session. However, most existing studies are not well-designed for modeling heterogeneous user behaviors and capturing the relationships between them in practical scenarios. To fill this gap, in this paper, we propose a novel graph-based method, namely Heterogeneous Information Crossing on Graphs (HICG). HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users' current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs. In addition, we also propose an enhanced version, named HICG-CL, which incorporates contrastive learning (CL) technique to enhance item representation ability. By utilizing the item co-occurrence relationships across different sessions, HICG-CL improves the recommendation performance of HICG. We conduct extensive experiments on three real-world recommendation datasets, and the results verify that (i) HICG achieves the state-of-the-art performance by utilizing multiple types of behaviors on the heterogeneous graph. (ii) HICG-CL further significantly improves the recommendation performance of HICG by the proposed contrastive learning module.