# word-embeddings Published articles for word-embeddings. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Semantic Search - Word Embeddings with OpenAI DevFeed: [Semantic Search - Word Embeddings with OpenAI]() Original publisher: [Read original article]() Author: umer Published: 2023-03-28T00:00:00Z Content type: tutorial Language: en Sources: [Code Ahoy - Articles]() Topics: [AI search](), [Natural language processing](), [Databases](), [sentence-transformers](), [Computer science]() Tags: [databases](), [embeddings](), [nlp](), [openai](), [search](), [techniques](), [vector](), [word-embeddings]() ### AI overview This introductory tutorial contrasts semantic search with lexical search, explaining how semantic search uses query context and intent to improve result relevance. It also introduces NLP, embeddings, and vector databases as implementation components. ### Source excerpt According to Wikipedia, Semantic Search denotes search with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query. For example a user is searching for the term "jaguar." A traditional keyword-based search engine might return results about the car manufacturer, the animal, or even the Jacksonville Jaguars football team. However, semantic search would analyze the context and intent behind the user's query, such as whether they are interested in cars or wildlife, and then prioritize results accordingly. In this blog post, we will explore the underlying principles of semantic search, discuss its advantages over other types of search, and examine real-world applications that are transforming the way we access and consume information. Lexical Search Engines Lexical (Traditional) search engines have served us well using keyword-based search methods, looking for matching exact words or phrases in users' queries with those in documents/database. For example, if we search for the term "computer science intro" in a lexical / traditional search engine, it will return results that match one or more of my search terms. As you can imagine, the keyword matching approach often falls short when it comes to understanding what the user actually meant, often producing less accurate results. Semantic Search Enter semantic search -- a context-aware search technology that aims to improve search results by focusing on understanding the meaning and context behind queries. When a user inputs the query "computer science intro" in a semantic search engine, it would first attempt to understand the intent behind the query. In this case, the user is likely looking for introductory resources related to computer science. Based on this understanding, the search engine would prioritize search results such as introductory computer science courses or textbooks or other ## Personalized Fishbowl Recommendations with Learned Embeddings: Part 2 DevFeed: [Personalized Fishbowl Recommendations with Learned Embeddings: Part 2]() Original publisher: [Read original article]() Author: Ahmad Khan Published: 2022-04-05T00:24:03Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [recommendation systems](), [Embeddings](), [Natural language processing](), [datasets]() Tags: [deep-learning](), [doc2vec](), [embeddings](), [model](), [nlp](), [personalization](), [quality](), [ranking](), [recommendations](), [recommender-systems](), [research](), [word-embeddings]() ### AI overview This engineering article explains how Fishbowl recommendations can use learned embeddings. It describes Doc2Vec-based post and user embeddings, collaborative signals from users with similar likes, cosine-similarity ranking, and additional personalization features such as employer, job title, work city, and feed information. It also introduces transfer learning for text content embeddings. ### Source excerpt Introduction In the previous blog post, we saw how we can utilize text based embeddings to help recommend posts to users on Fishbowl, a professional networking community that was recently acquired by Glassdoor, in which working professionals can have workplace related conversations with other peers in industry. On Fishbowl, users can anonymously write what's on their mind in posts and also comment on posts from other anonymous users in what we call "bowls" or "feeds"; a collection of posts related to a certain industry or topic. Previously we discussed how in the absence of clear negative signals from click stream data we cannot as easily use a supervised learning setup to rank items to users. Given the inputs we used were text based, we can instead use more unsupervised methods like Doc2Vec [1] to generate post text embeddings. We can treat the text of the posts as individual documents and use those to train a Doc2Vec model to generate a post text embedding. For users, we can take the average post text embedding of the posts the user liked and consider that as the user embedding. We can also add the user embeddings of other users who liked a post into our post embedding calculation so we incorporate some "collaborative" notion of what other similar users liked as opposed to a pure content similarity ranking. We can next compute the cosine similarity between the user and posts embeddings and use the similarity score to rank posts to recommend to users. Ranking via Embeddings Overview However, such an approach can have some shortcomings. First the text of the post is just one of many features we can use for personalization. We can also leverage other user provided information when users agree to sign up like the employer, job title, work city of a user as features. In addition, the bowl or feed name and its description can be considered important features for posts as posts under the same feed tend to be about similar topics. Second, the previous Doc2Vec approach tra ## Personalized Fishbowl Recommendations with Learned Embeddings: Part 1 DevFeed: [Personalized Fishbowl Recommendations with Learned Embeddings: Part 1]() Original publisher: [Read original article]() Author: Ahmad Khan Published: 2022-01-07T21:48:24Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [recommendation systems](), [personalization](), [Machine learning](), [Embeddings]() Tags: [doc2vec](), [embeddings](), [fishbowl](), [glassdoor](), [machine-learning](), [personalization](), [recommendation-system](), [recommendations](), [word-embeddings]() ### AI overview Glassdoor's Fishbowl team describes the recommendation problem created by a growing volume of posts and explains the move from recency and global-popularity rankings toward personalized recommendations using machine learning and learned embeddings. ### Source excerpt Glassdoor recently acquired Fishbowl, a professional networking community where working professionals can have workplace related conversations with other peers in the industry. Fishbowl users can anonymously write posts and see posts from other anonymous users in what we call "bowls": a collection of posts related to a certain workplace, industry or topic. Bowls can be an effective way to gain insights into workplace topics and conversations. The anonymous nature of the app can further encourage honest and frank discussion on topics users may otherwise feel uncomfortable discussing. Users can subscribe to different bowls and then see new posts from their subscribed bowls show up in their main home feed when they open the app. Every day Fishbowl users post thousands of new posts. With a growing and increasingly active user base that number will keep increasing. Surfacing the most interesting content to users can therefore become increasingly challenging with scale and a lack of personalization can detract from the overall user experience. Given the large number of possible posts to recommend and the small number of posts that can be surfaced to the user in app at any time, we have a typical recommendation system problem. From Global Rankings to Personalized Recommendations To personalize the posts recommended to a user we decided to use Machine Learning shortly after Glassdoor's acquisition. Prior to this Fishbowl just used the recency and global popularity of a post to sort what to show users in app. At the time of starting the project we also did not collect any explicit user click data that could have defined our problem into a classic supervised learning problem (e.g: predicting the probability of a user clicking a post given they saw the post). While we collected data on what posts a user anonymously liked or commented on we did not collect good data on if they saw or clicked a post at the time. Why is this important? If training a supervised model, we can use s ## Bias in word embeddings DevFeed: [Bias in word embeddings]() Original publisher: [Read original article]() Author: adriancolyer Published: 2020-12-08T14:32:00Z Content type: article Language: en Sources: [Adrian Colyer]() Topics: [Embeddings](), [Machine learning](), [Algorithms, Complexity]() Tags: [algorithm](), [bias](), [dataset](), [embeddings](), [machine-learning](), [model](), [train](), [uncategorized](), [word-embeddings]() ### AI overview This article summarizes research on bias in word embeddings, explaining how bias in training text can be encoded in embeddings, transferred to later algorithms, and produce socially discriminatory decisions. It also discusses detecting, measuring, and mitigating that bias. ### Source excerpt Bias in word embeddings, Papakyriakopoulos et al., FAT*'20 There are no (stochastic) parrots in this paper, but it does examine bias in word embeddings, and how that bias carries forward into models that are trained using them. There are definitely some dangers to be aware of here, but also some cause for hope as we ... Continue reading Bias in word embeddings