Sequential recommendation based on multi-interest framework models the user's recent interaction sequence into multiple different interest vectors, since a single low-dimensional vector cannot fully represent the diversity of user interests. However, most existing models only intercept users' recent interaction behaviors as training data, discarding a large amount of historical interaction sequences. This may raise two issues. On the one hand, data reflecting multiple interests of users is missing; on the other hand, the co-occurrence between items in historical user-item interactions is not fully explored. To tackle the two issues, this paper proposes a novel sequential recommendation model called "Global Interaction Aware Multi-Interest Framework for Sequential Recommendation (GIMIRec)". Specifically, a global context extraction module is firstly proposed without introducing any external information, which calculates a weighted co-occurrence matrix based on the constrained co-occurrence number of each item pair and their time interval from the historical interaction sequences of all users and then obtains the global context embedding of each item by using a simplified graph convolution. Secondly, the time interval of each item pair in the recent interaction sequence of each user is captured and combined with the global context item embedding to get the personalized item embedding. Finally, a self-attention based multi-interest framework is applied to learn the diverse interests of users for sequential recommendation. Extensive experiments on the three real-world datasets of Amazon-Books, Taobao-Buy and Amazon-Hybrid show that the performance of GIMIRec on the Recall, NDCG and Hit Rate indicators is significantly superior to that of the state-of-the-art methods. Moreover, the proposed global context extraction module can be easily transplanted to most sequential recommendation models.

For this project we are using The Million Playlist Dataset (MPD) released by Spotify. As it name implies, the dataset consists of one million playlists and each playlists contains n number of songs and additional metadata is included as well such as title of the playlist, duration, number of songs, number of artists, etc. This dataset was created by sampling playlists from the billions of playlists that Spotify users have created over the years.

In the past decade, automatic product description generation for e-commerce have witnessed significant advancement. As the services provided by e-commerce platforms become diverse, it is necessary to dynamically adapt the patterns of descriptions generated. The selling point of products is an important type of product description for which the length should be as short as possible while still conveying key information. In addition, this kind of product description should be eye-catching to the readers. Currently, product selling points are normally written by human experts. Thus, the creation and maintenance of these contents incur high costs. These costs can be significantly reduced if product selling points can be automatically generated by machines. In this paper, we report our experience developing and deploying the Intelligent Online Selling Point Extraction (IOSPE) system to serve the recommendation system in the JD.com e-commerce platform. Since July 2020, IOSPE has become a core service for 62 key categories of products (covering more than 4 million products). So far, it has generated more than 0.1 billion selling points, thereby significantly scaling up the selling point creation operation and saving human labour. These IOSPE generated selling points have increased the click-through rate (CTR) by 1.89\% and the average duration the customers spent on the products by more than 2.03\% compared to the previous practice, which are significant improvements for such a large-scale e-commerce platform.

Cultural code-switching concerns how we adjust our overall behaviours, manners of speaking, and appearance in response to a perceived change in our social environment. We defend the need to investigate cultural code-switching capacities in artificial intelligence systems. We explore a series of ethical and epistemic issues that arise when bringing cultural code-switching to bear on artificial intelligence. Building upon Dotson's (2014) analysis of testimonial smothering, we discuss how emerging technologies in AI can give rise to epistemic oppression, and specifically, a form of self-silencing that we call 'cultural smothering'. By leaving the socio-dynamic features of cultural code-switching unaddressed, AI systems risk negatively impacting already-marginalised social groups by widening opportunity gaps and further entrenching social inequalities.

Conversational recommendation systems (CRS) engage with users by inferring user preferences from dialog history, providing accurate recommendations, and generating appropriate responses. Previous CRSs use knowledge graph (KG) based recommendation modules and integrate KG with language models for response generation. Although KG-based approaches prove effective, two issues remain to be solved. First, KG-based approaches ignore the information in the conversational context but only rely on entity relations and bag of words to recommend items. Second, it requires substantial engineering efforts to maintain KGs that model domain-specific relations, thus leading to less flexibility. In this paper, we propose a simple yet effective architecture comprising a pre-trained language model (PLM) and an item metadata encoder. The encoder learns to map item metadata to embeddings that can reflect the semantic information in the dialog context. The PLM then consumes the semantic-aligned item embeddings together with dialog context to generate high-quality recommendations and responses. Instead of modeling entity relations with KGs, our model reduces engineering complexity by directly converting each item to an embedding. Experimental results on the benchmark dataset ReDial show that our model obtains state-of-the-art results on both recommendation and response generation tasks.

You don't have to own a robot vacuum or a digital assistant like Alexa or Siri to use artificial intelligence. In fact, AI has become part of our everyday lives in ways we don't even notice, let alone control. When you check your news feed on Facebook or search the internet on Google, you're interacting with AI. It offers great benefits, like robots assisting during surgery, but also gives rise to troubling moral questions. Henley, the author or coauthor of more than twenty books, brings an impressive background to this weighty topic.

Jenée Desmond-Harris is online weekly to chat live with readers. Here's an edited transcript of this week's chat. Q. Hurt and betrayed: I recently found out my husband of seven years has been on adult dating sites and OnlyFans. I found multiple purchases from these sites over a year-and-a-half span and had no idea about it. He doesn't think he cheated since he didn't physically ever meet these women; I guess he only bought videos or pictures.

Coinbase has built the world's leading compliant cryptocurrency platform serving over 73 million accounts in more than 100 countries. With multiple successful products, and our vocal advocacy for blockchain technology, we have played a major part in mainstream awareness and adoption of cryptocurrency. We are proud to offer an entire suite of products that are helping build the cryptoeconomy and increase economic freedom around the world. There are a few things we look for across all hires we make at Coinbase, regardless of role or team. First, we look for signals that a candidate will thrive in a culture like ours, where we default to trust, embrace feedback, disrupt ourselves, and expect sustained high performance because we play as a championship team.

With close to driverless cars, robots to automate menial tasks, smart virtual assistants to solve your doubts in milliseconds or robots to map diseases, AI is the heart of most intelligent products and services today. The global AI market was valued at $62.35 billion in 2020 and is only expected to develop at a compound annual growth rate of 40 per cent in the next decade. Given this colossal maturation, it comes with no surprise that VC firms are investing in the technology at a heavy pace. VC investors across public and private sectors are becoming increasingly interested in having their share in this tremendously growing sector. PWC's 2020 Money Tree Report outlined AI as an emerging area in the market, with $11.5 billion being invested in AI companies during the first three quarters of last year.

Graph neural networks (GNNs) have been widely applied in the recommendation tasks and have obtained very appealing performance. However, most GNN-based recommendation methods suffer from the problem of data sparsity in practice. Meanwhile, pre-training techniques have achieved great success in mitigating data sparsity in various domains such as natural language processing (NLP) and computer vision (CV). Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations. However, pre-training GNNs for recommendations face unique challenges. For example, user-item interaction graphs in different recommendation tasks have distinct sets of users and items, and they often present different properties. Therefore, the successful mechanisms commonly used in NLP and CV to transfer knowledge from pre-training tasks to downstream tasks such as sharing learned embeddings or feature extractors are not directly applicable to existing GNN-based recommendations models. To tackle these challenges, we delicately design an adaptive graph pre-training framework for localized collaborative filtering (ADAPT). It does not require transferring user/item embeddings, and is able to capture both the common knowledge across different graphs and the uniqueness for each graph. Extensive experimental results have demonstrated the effectiveness and superiority of ADAPT.