Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences. While traditional RSs rely on implicit user feedback signals, conversational RSs interact with users in natural language. In this work, we develop a comPelling, Precise, Personalized, Preference-relevant language model (P4LM) that recommends items to users while putting emphasis on explaining item characteristics and their relevance. P4LM uses the embedding space representation of a user's preferences to generate compelling responses that are factually-grounded and relevant w.r.t. the user's preferences. Moreover, we develop a joint reward function that measures precision, appeal, and personalization, which we use as AI-based feedback in a reinforcement learning-based language model framework. Using the MovieLens 25M dataset, we demonstrate that P4LM delivers compelling, personalized movie narratives to users.

今日は、ChatGPTのAPIを利用して、プログラムのバグを自動修復するWolverineというプログラムの紹介となります。 今回は、下記のサイトを参考にさせて頂いております。 今回、利用したコードは以下となります。 python -m venv venv venv\Scripts\activate git clone https://github.com/biobootloader/wolverine.git cd wolverine pip install -r requirements.txt 次に、wolverineフォルダの下に、openai_key

Sony issued a disclaimer ahead of Thursday's showcase that the next generation of PlayStation's virtual reality tech wouldn't make an appearance. Also absent was a much-anticipated update about the PS5′s expanded storage feature. Over the summer, Sony launched a beta program for the long-awaited feature, which allows users to expand their device's storage using specific M. 2 solid-state drives, for select users in the United States, Canada, France, Japan, Germany and the U.K. When it will roll out to the general public remains unclear.

The conservation of wildlife corridors between existing habitat preserves is important for combating the effects of habitat loss and fragmentation facing species of concern. We introduce the Steiner Multigraph Problem to model the problem of minimum-cost wildlife corridor design for multiple species with different landscape requirements. This problem can also model other analogous settings in wireless and social networks. As a generalization of Steiner forest, the goal is to find a minimum-cost subgraph that connects multiple sets of terminals. In contrast to Steiner forest, each set of terminals can only be connected via a subset of the nodes. Generalizing Steiner forest in this way makes the problem NP-hard even when restricted to two pairs of terminals. However, we show that if the node subsets have a nested structure, the problem admits a fixed-parameter tractable algorithm in the number of terminals. We successfully test exact and heuristic solution approaches on a wildlife corridor instance for wolverines and lynx in western Montana, showing that though the problem is computationally hard, heuristics perform well, and provably optimal solutions can still be obtained.