Patents by Inventor Mounia Lalmas
Mounia Lalmas has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Publication number: 20240152545Abstract: An electronic system stores metadata for a plurality of media items, including, for each media item of the plurality of media items, at least one categorical identifier from a set of categorical identifiers. For a user of the media-providing service, the electronic system (i) determines a distribution of interests of the user with respect to the set of categorical identifiers; (ii) generates a network graph configured to represent a calibrated media item selection task, wherein the network graph represents respective relevance scores for each respective media item of the plurality of media items and the distribution of interests of the user with respect to the categorical identifiers; (iii) selects a set of media items from the plurality of media items to recommend to the user by solving for a maximum flow of the network graph; and (iv) provides the set of media items as recommendations to the user.Type: ApplicationFiled: February 8, 2023Publication date: May 9, 2024Inventors: Tony Jebara, Himan Abdollahpouri, Zahra Nazari, Alexander Zachary Gain, Maria Dimakopoulou, Benjamin Carterette, Mounia Lalmas-Roelleke, Clay Gibson
-
Patent number: 11875273Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for machine learning (ML) classification of digital content for mobile communication devices, such as implemented in connection with one or more computing and/or communication networks and/or protocols.Type: GrantFiled: March 29, 2017Date of Patent: January 16, 2024Assignee: Yahoo Ad Tech LLCInventors: Marc Bron, Mounia Lalmas, Huw Evans, Mahlon Chute, Miriam Redi, Fabrizio Silvestri
-
Patent number: 11869040Abstract: The present teaching relates to analyzing user behavior associated with web contents. Information related to user interactions associated with a content item placed on a reference property is first obtained. A measurement associated with each user interaction of the content item is determined based on the obtained information. An analyzing model for the content item which characterizes statistics of the measurements associated with the content item is further constructed. A measurement threshold to be used to determine a cost of placing the content item on a target property is further determined using the constructed analyzing model.Type: GrantFiled: June 30, 2015Date of Patent: January 9, 2024Assignee: YAHOO AD TECH LLCInventors: Gabriele Tolomei, Ayman Farahat, Mounia Lalmas
-
Patent number: 11782988Abstract: Methods, systems and computer program products are provided for query understanding. A non-focused query quantifier generates non-focused query features that quantify a non-focused query and a non-focused query predictor generates a prediction associated with the non-focused query based on the non-focused query features.Type: GrantFiled: September 21, 2020Date of Patent: October 10, 2023Assignee: Spotify ABInventors: Federico Tomasi, Rishabh Mehrotra, Brian Christian Peter Brost, Aasish Kumar Pappu, Hugo Flávio Ventura Galvão, Mounia Lalmas-Roelleke
-
Patent number: 11782968Abstract: An electronic device stores a plurality of vector representations for respective media content items in a vector space, where each vector represents a media content item. The electronic device receives a first set of input parameters representing a previous session of a user of the media-providing service where the previous session included two or more of the respective media content items. The electronic device then receives a second set of input parameters representing a current context of the user and provides the first set of input parameters and the second set of input parameters to a neural network to generate a prediction vector for a current session. The prediction vector is embedded in the vector space.Type: GrantFiled: February 12, 2020Date of Patent: October 10, 2023Assignee: Spotify ABInventors: Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Christian Peter Brost, Federico Tomasi, Mounia Lalmas-Roelleke
-
Patent number: 11727221Abstract: A system implements a dynamic correlated topic model (DCTM) to model an evolution of topic popularity, topic representation, and topic correlation within a set of documents, or other dataset, that spans a period of time. For example, the DCTM receives the set of documents and a quantity of topics for modeling. The DCTM processes the set by analyzing words of the documents, identifying word clusters representing the topics, and computing, for each topic, various distributions using continuous processes to capture a popularity, representation, and correlation with other topics across the period of time. In other examples, the dataset are user listening sessions comprised of media content items. Media content metadata (e.g., artist or genre) of the media content items, similar to words of a document, can be analyzed and clustered to represent topics for modeling by the DCTM.Type: GrantFiled: July 17, 2020Date of Patent: August 15, 2023Assignee: Spotify ABInventors: Praveen Chandar Ravichandran, Mounia Lalmas-Roelleke, Federico Tomasi, Zhenwen Dai, Gal Levy-Fix
-
Patent number: 11556828Abstract: An electronic device for a first session of a user, for each of a plurality of lists of media content items, determines a respective value for each objective of a first set of objectives and a second set of objectives by accessing contextual data for the first session of the user. The first set of objectives corresponds to the user and the second set of objectives corresponds to a second party distinct from the user. The electronic device, using a multi-arm bandit model, identifies a first list of media content items, from the plurality of lists of media content items, to present to the user, including: calculating a score for each list in the plurality of lists of media items; and probabilistically selecting the first list of media content items according to the respective scores corresponding to the respective lists in the plurality of lists of media items.Type: GrantFiled: February 8, 2021Date of Patent: January 17, 2023Assignee: Spotify ABInventors: Rishabh Mehrotra, Niannan Xue, Mounia Lalmas-Roelleke
-
Patent number: 11540017Abstract: A method of recommending media items to a user is provided. The method includes receiving historical data for a user of a media providing service. The historical data indicates past interactions of the user with media items. The method includes generating a model of the user. The model includes a first set of parameters, each of the first set of parameters quantifying a predicted latent preference of the user for a respective media item provided by the media providing service. The method includes evaluating the predicted latent preferences of the user for the respective media items against the historical data indicating the past interactions of the user with the media items provided by the media providing service. The method includes selecting a recommender system from a plurality of recommender systems using the model of the user, including the first set of parameters. The method includes providing a media item to a second user using the selected recommender system.Type: GrantFiled: May 19, 2021Date of Patent: December 27, 2022Assignee: Spotify ABInventors: Dmitrii Moor, Rishabh Mehrotra, Mounia Lalmas-Roelleke
-
Publication number: 20220147716Abstract: A system implements a dynamic word correlated topic model (DWCTM) to model an evolution of topic popularity, word embedding, and topic correlation within a set of documents, or other dataset, that spans a period of time. For example, the DWCTM receives the set of documents and a quantity of topics for modeling. The DWCTM processes the set computing, for each topic, various distributions to capture a popularity, word embedding, and correlation with other topics across the period of time. In other examples, a dataset of user listening sessions comprised of media content items for modeling by the DWCTM. Media content metadata (e.g., artist or genre) of the media content items, similar to words of a document, can be modeled by the DWCTM.Type: ApplicationFiled: November 15, 2021Publication date: May 12, 2022Inventors: Federico TOMASI, Zhenwen DAI, Mounia LALMAS-ROELLEKE
-
Publication number: 20220108125Abstract: Disclosed examples include an automated online experimentation mechanism that can perform model selection from a large pool of models with a relatively small number of online experiments. The probability distribution of the metric of interest that contains the model uncertainty is derived from a Bayesian surrogate model trained using historical logs. Disclosed techniques can be applied to identify a superior model by sequentially selecting and deploying a list of models from the candidate set that balance exploration-exploitation.Type: ApplicationFiled: October 5, 2020Publication date: April 7, 2022Inventors: Zhenwen Dai, Praveen Chandar Ravichandran, Ghazal Fazelnia, Benjamin Carterette, Mounia Lalmas-Roelleke
-
Publication number: 20220092118Abstract: Methods, systems and computer program products are provided for query understanding. A non-focused query quantifier generates non-focused query features that quantify a non-focused query and a non-focused query predictor generates a prediction associated with the non-focused query based on the non-focused query features.Type: ApplicationFiled: September 21, 2020Publication date: March 24, 2022Inventors: Federico Tomasio, Rishabh Mehrotra, Brian Christian Peter Brost, Aasish Kumar Pappu, Hugo Flávio Ventura Galvão, Mounia Lalmas-Roelleke
-
Publication number: 20220019922Abstract: An electronic device for a first session of a user, for each of a plurality of lists of media content items, determines a respective value for each objective of a first set of objectives and a second set of objectives by accessing contextual data for the first session of the user. The first set of objectives corresponds to the user and the second set of objectives corresponds to a second party distinct from the user. The electronic device, using a multi-arm bandit model, identifies a first list of media content items, from the plurality of lists of media content items, to present to the user, including: calculating a score for each list in the plurality of lists of media items; and probabilistically selecting the first list of media content items according to the respective scores corresponding to the respective lists in the plurality of lists of media items.Type: ApplicationFiled: February 8, 2021Publication date: January 20, 2022Inventors: Rishabh MEHROTRA, Niannan XUE, Mounia LALMAS-ROELLEKE
-
Publication number: 20220019750Abstract: A system implements a dynamic correlated topic model (DCTM) to model an evolution of topic popularity, topic representation, and topic correlation within a set of documents, or other dataset, that spans a period of time. For example, the DCTM receives the set of documents and a quantity of topics for modeling. The DCTM processes the set by analyzing words of the documents, identifying word clusters representing the topics, and computing, for each topic, various distributions using continuous processes to capture a popularity, representation, and correlation with other topics across the period of time. In other examples, the dataset are user listening sessions comprised of media content items. Media content metadata (e.g., artist or genre) of the media content items, similar to words of a document, can be analyzed and clustered to represent topics for modeling by the DCTM.Type: ApplicationFiled: July 17, 2020Publication date: January 20, 2022Applicant: Spotify ABInventors: Praveen Chandar Ravichandran, Mounia Lalmas-Roelleke, Federico Tomasi, Zhenwen Dai, Gal Levy-Fix
-
Publication number: 20220012565Abstract: A reinforcement learning ranker can take into account previously-recommended media content items to produce a ranked list of media content items to recommend next. The ranker finds a policy that gives the probability of sampling a media content item given a state. The policy is learned such that it maximizes a reward. A reward function associated with the media content item can be defined with respect to whether the user finds the media content item relevant (likelihood that the user will like the media content item) and a diversity score of the media content item.Type: ApplicationFiled: May 14, 2021Publication date: January 13, 2022Applicant: Spotify ABInventors: Christian Hansen, Casper Hansen, Brian Christian Peter Brost, Lucas Maystre, Mounia Lalmas-Roelleke, Rishabh Mehrotra
-
Patent number: 11157836Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for changing a classification of a landing page, such as via, for example, identifying features of the landing page, such as to predict a binary classification of the landing page as to post-click user experience. One or more adjustments to features of the landing page may be determined, such as using a machine learning approach, by way of non-limiting example.Type: GrantFiled: February 28, 2017Date of Patent: October 26, 2021Assignee: VERIZON MEDIA INC.Inventors: Gabriele Tolomei, Andy Haines, Mounia Lalmas, Fabrizio Silvestri
-
Patent number: 11157967Abstract: The present teaching relates to providing content supply adjustment. A first dataset associated with user interactions directed to one or more content items placed on a target property and a second dataset associated with user interactions directed to the one or more content items places on a reference property are received for evaluation. Further, a cost of placing the one or more content items on the target property is determined based on the first and second datasets.Type: GrantFiled: June 30, 2015Date of Patent: October 26, 2021Assignee: VERIZON MEDIA INC.Inventors: Ayman Farahat, Mounia Lalmas, Gabriele Tolomei
-
Patent number: 11113714Abstract: A filtering machine receives sponsored content and filters the sponsored content according to a quality metric generated by quality model circuitry and assigned to the instance of sponsored content. The quality model circuitry generates the quality metric in accordance with historical feedback received about other sponsored content and a collection of quality factors pertaining to the sponsored content. Based on the quality metric for the sponsored content, the filtering machine can effect service of the sponsored content to a user device for display thereon.Type: GrantFiled: December 30, 2015Date of Patent: September 7, 2021Assignee: Verizon Media Inc.Inventors: Ke Zhou, Miriam Redi, Mounia Lalmas, Puneet Mohan Sangal
-
Publication number: 20210248173Abstract: An electronic device stores a plurality of vector representations for respective media content items in a vector space, where each vector represents a media content item. The electronic device receives a first set of input parameters representing a previous session of a user of the media-providing service where the previous session included two or more of the respective media content items. The electronic device then receives a second set of input parameters representing a current context of the user and provides the first set of input parameters and the second set of input parameters to a neural network to generate a prediction vector for a current session. The prediction vector is embedded in the vector space.Type: ApplicationFiled: February 12, 2020Publication date: August 12, 2021Inventors: Casper HANSEN, Christian HANSEN, Lucas MAYSTRE, Rishabh MEHROTRA, Brian Christian Peter BROST, Federico TOMASI, Mounia LALMAS-ROELLEKE
-
Patent number: 10755303Abstract: An online advertising system receives an advertisement from an advertiser. The system analyzes the advertisement, extracts its features and provides to the advertiser a quality rating for the advertisement which depends on a user engagement factor such as the predicted dwell time for the ad, given its features. The system further provides to the advertiser suggestions for improvements to the advertisement, such as a list of actionable guidelines that can improve the expected dwell time of the ad, and likely its conversion rate.Type: GrantFiled: November 30, 2015Date of Patent: August 25, 2020Assignee: Oath Inc.Inventors: Michele Trevisiol, Gabriele Tolomei, Nicola Barbieri, Mounia Lalmas, Puneet Mohan Sangal, Fabrizio Silvestri
-
Publication number: 20180285747Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for machine learning (ML) classification of digital content for mobile communication devices, such as implemented in connection with one or more computing and/or communication networks and/or protocols.Type: ApplicationFiled: March 29, 2017Publication date: October 4, 2018Inventors: Marc Bron, Mounia Lalmas, Huw Evans, Mahlon Chute, Miriam Redi, Fabrizio Silvestri