# US 2020/0401643 A1 · Position debiasing using inverse propensity weight in machine-learned model > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401643 A1 - Document ID: US20200401643A1 - Status: Published application - Kind: A1 - Application number: 16/449,135 - Filed: 2019-06-21 - Published: 2020-12-24 - Earliest claimed: 2019-06-21 - Assignees: Microsoft Technology Licensing, LLC - Applicants: Microsoft Technology Licensing, LLC - Inventors: Dan Liu; Daniel Sairom Krishnan Hewlett; Qi Guo - Web page: https://patentgrep.com/patent/US20200401643A1 ## CPC - G06F 16/9538: Presentation of query results - G06N 3/084: Backpropagation, e.g. using gradient descent - G06F 16/906: Clustering; Classification - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/047: Probabilistic or stochastic networks - G06N 3/09: Supervised learning - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract In an example embodiment, position bias is addressed by introducing an inverse propensity weight into a loss function used to train a machine-learned model. This inverse propensity weight essentially increases the weight of candidates in the training data that were presented lower in a list of candidates. This achieves the benefit of counteracting the position bias and increases the effectiveness of the machine-learned model in generating scores for future candidates. In a further example embodiment, a function is generated for the inverse propensity weight based on responses to contact requests from recruiters. In other words, while the machine learned-model may factor in both the likelihood that a recruiter will want to contact a candidate and the likelihood that a candidate will respond to such a contact, the function generated for the inverse propensity weight will be based only on training data where the candidate actually responded to a contact. ## Claims ### Claim 1 A system for returning search results in an online computer system, the system comprising: a computer readable medium having instructions stored there on, which, when executed by a processor, cause the system to: generate a first inverse propensity weight function from a total number of responses generated by members of an online service in response to communication requests from other members, at each position in a ranking of returned member profiles; for each sample member profile in a plurality of sample member profiles: parse the sample member profile and the activity and usage information pertaining to actions taken by a member corresponding to the sample member profile in an online service to extract a first set of one or more features; parse an associated query to obtain one or more query features; feed sample member labels, the extracted first set of one or more features and the extracted one or more query features into a first machine learning algorithm to train a machine-learned model to output a score indicative of a probability that a searcher will select a potential search result corresponding to the sample member profile and a probability that a member corresponding to the sample member profile will respond to a communication from a searcher, the training including using the first inverse propensity weight function to evaluate a loss function. ### Claim 2 (depends on 1) The system of claim 1, wherein the instructions further cause the system to: obtain a plurality of candidate search results in response to a query corresponding to a searcher; for each candidate search result from the plurality of candidate search results: obtain activity and usage information for a member corresponding to the candidate search result; parse the candidate search result and the activity and usage information for the member corresponding to the candidate search result to extract a second set of one or more features; parse the query to obtain one or more query features; feed the extracted second set of one or more features and one or more query features into the machine-learned model, outputting a score for the candidate search result; rank the plurality of candidate search results by their corresponding scores; and return one or more of the plurality of candidate search results based on the ranking. ### Claim 3 (depends on 1) The system of claim 1, wherein the first inverse propensity weight function is generated only for positions in the ranking higher than or equal to a preset position; and wherein instructions further comprise: generating a second inverse propensity weight function, for positions in the ranking lower than the preset position, from a metric other than the total number of responses generated by members in response to communication requests from other members, at each position in a ranking of returned member profiles; and wherein the training further uses the second inverse propensity weight function to evaluate the loss function. ### Claim 4 (depends on 3) The system of claim 3, wherein the second inverse propensity weight function is generated by modeling a curve to fit a curve generated by the first inverse propensity weight function. ### Claim 5 (depends on 3) The system of claim 3, wherein the second inverse propensity weight function assigns a weight; to all positions in the ranking lower than the preset position, that is equal to a weight assigned by the first inverse propensity weight function to the position the ranking equal to the preset position. ### Claim 6 (depends on 1) The system of claim 1, wherein the machine-learned model is a deep neural network. ### Claim 7 (depends on 1) The system of claim 1, wherein the machine-learned model is a convolutional neural network. ### Claim 8 A computer-implemented method for returning search results in an online computer system, the method comprising: generating a first inverse propensity weight function from a total number of responses generated by members of an online service in response to communication requests from other members, at each position in a ranking of returned member profiles; for each sample member profile in a plurality of sample member profiles: parsing the sample member profile and the activity and usage information pertaining to actions taken by a member corresponding to the sample member profile in an online service to extract a first set of one or more features; parsing an associated query to obtain one or more query features; feeding sample member labels, the extracted first set of one or more features and the extracted one or more query features into a first machine learning algorithm to train a machine-learned model to output a score indicative of a probability that a searcher will select a potential search result corresponding to the sample member profile and a probability that a member corresponding to the sample member profile will respond to a communication from a searcher, the training including using the first inverse propensity weight function to evaluate a loss function. ### Claim 9 (depends on 8) The method of claim 8, wherein the instructions further cause the system to: obtain a plurality of candidate search results in response to a query corresponding to a searcher; for each candidate search result from the plurality of candidate search results: obtain activity and usage information for a member corresponding to the candidate search result; parse the candidate search result and the activity and usage information for the member corresponding to the candidate search result to extract a second set of one or more features; parse the query to obtain one or more query features; feed the extracted second set of one or more features and one or more query features into the machine-learned model, outputting a score for the candidate search result; rank the plurality of candidate search results by their corresponding scores; and return one or more of the plurality of candidate search results based on the ranking. ### Claim 10 (depends on 8) The method of claim 8, wherein the first inverse propensity weight function is generated only for positions in the ranking higher than or equal to a preset position; and wherein instructions further comprise: generating a second inverse propensity weight function, for positions in the ranking lower than the preset position, from a metric other than the total number of responses generated by members in response to communication requests from other members, at each position in a ranking of returned member profiles; and wherein the training further uses the second inverse propensity weight function to evaluate the loss function. ### Claim 11 (depends on 10) The method of claim 10, wherein the second inverse propensity weight function is generated by modeling a curve to fit a curve generated by the first inverse propensity weight function. ### Claim 12 (depends on 10) The method of claim 10, wherein the second inverse propensity weight function assigns a weight, to all positions in the ranking lower than the preset position, that is equal to a weight assigned by the first inverse propensity weight function to the position the ranking equal to the preset position. ### Claim 13 (depends on 8) The method of claim 8; wherein the machine-learned model is a deep neural network. ### Claim 14 (depends on 8) The method of claim 8, wherein the machine-learned model is a convolutional neural network. ### Claim 15 A non-transitory machine-readable storage medium comprising instructions, which when implemented by one or more machines, cause the one or more machines to perform operations comprising: generating a first inverse propensity weight function from a total number of responses generated by members of an online service in response to communication requests from other members, at each position in a ranking of returned member profiles; for each sample member profile in a plurality of sample member profiles: parsing the sample member profile and the activity and usage information pertaining to actions taken by a member corresponding to the sample member profile in an online service to extract a first set of one or more features; parsing an associated query to obtain one or more query features; feeding sample member labels, the extracted first set of one or more features and the extracted one or more query features into a first machine learning algorithm to train a machine-learned model to output a score indicative of a probability that a searcher will select a potential search result corresponding to the sample member profile and a probability that a member corresponding to the sample member profile will respond to a communication from a searcher, the training including using the first inverse propensity weight function to evaluate a loss function. ### Claim 16 (depends on 15) The non-transitory machine-readable storage medium of claim 15, wherein the instructions further cause the system to: obtain a plurality of candidate search results in response to a query corresponding to a searcher; for each candidate search result from the plurality of candidate search results: obtain activity and usage information for a member corresponding to the candidate search result; parse the candidate search result and the activity and usage information for the member corresponding to the candidate search result to extract a second set of one or more features; parse the query to obtain one or more query features; feed the extracted second set of one or more features and one or more query features into the machine-learned model, outputting a score for the candidate search result; rank the plurality of candidate search results by their corresponding scores; and return one or more of the plurality of candidate search results based on the ranking. ### Claim 17 (depends on 15) The non-transitory machine-readable storage medium of claim 15, wherein the first inverse propensity weight function is generated only for positions in the ranking higher than or equal to a preset position; and wherein instructions further comprise: generating a second inverse propensity weight function, for positions in the ranking lower than the preset position, from a metric other than the total number of responses generated by members in response to communication requests from other members, at each position in a ranking of returned member profiles; and wherein the training further uses the second inverse propensity weight function to evaluate the loss function. ### Claim 18 (depends on 17) The non-transitory machine-readable storage medium of claim 17, wherein the second inverse propensity weight function is generated by modeling a curve to fit a curve generated by the first inverse propensity weight function. ### Claim 19 (depends on 17) The non-transitory machine-readable storage medium of claim 17, wherein the second inverse propensity weight function assigns a weight, to all positions in the ranking lower than the preset position, that is equal to a weight assigned by the first inverse propensity weight function to the position the ranking equal to the preset position. ### Claim 20 (depends on 15) The non-transitory machine-readable storage medium of claim 15, wherein the machine-learned model is a deep neural network.