Software that uses personalized information pertaining to a user to determine how familiar (or “novel” or “surprising”) a new artifact will be to the user, by performing the following steps: (i) receiving a first dataset pertaining to a first user; (ii) building, utilizing the first dataset, an ontology of artifacts known to the first user, where the ontology includes a domain of food and a plurality of artifacts that include food recipes, and where the artifacts have corresponding characteristics that include food ingredients; (iii) calculating a prior probability distribution for each artifact of the ontology using a probabilistic familiarity algorithm; and (iv) calculating a probabilistic familiarity value for the first artifact with respect to the first user by adding the first artifact to the set of artifacts and calculating the first artifact's prior probability distribution using the probabilistic familiarity algorithm.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving a first dataset pertaining to a first user; building, utilizing the first dataset, an ontology of artifacts known to the first user, where the ontology includes a domain of food and a plurality of artifacts that include food recipes, and where the artifacts have corresponding characteristics that include food ingredients; calculating a prior probability distribution for each artifact of the ontology using a probabilistic familiarity algorithm, wherein calculating the prior probability distribution for a given artifact using the probabilistic familiarity algorithm includes: determining an ontology closeness representing an amount of closeness between the given artifact and a set of artifacts that are determined to be similar to the given artifact based on the respective characteristics of the artifacts, determining a social proximity measure representing an amount of closeness between the first user and a set of persons within a social network of the first user, determining a temporal proximity measure representing an amount of closeness between a time the given artifact was observed by the first user and a set of time points prior to the time the given artifact was observed by the first user, and utilizing the ontology closeness, the social proximity measure, and the temporal proximity measure to create a weighted frequency for the given artifact, the weighted frequency corresponding to the prior probability distribution; receiving the identity of a first artifact, wherein the first artifact is not included in the ontology; and calculating a probabilistic familiarity value for the first artifact with respect to the first user by adding the first artifact to the set of artifacts and calculating the first artifact's prior probability distribution using the probabilistic familiarity algorithm. 2. The method of claim 1, further comprising: determining if the probabilistic familiarity value is below a first probabilistic familiarity threshold, wherein if the probabilistic familiarity value is below the first probabilistic familiarity threshold, the first artifact is added to a set of artifact(s) that are not familiar to the first user. 3. The method of claim 1, wherein: the probabilistic familiarity algorithm is a Bayesian surprise-based algorithm. 4. The method of claim 1, wherein: the first dataset includes at least one piece of personalized information for the first user; and the at least one piece of personalized information is selected from the group consisting of: activity by the user on a social networking service (SNS); activity viewable by the user on a SNS; online purchase history of the user; online browser history of the user; website content created by the user; and content created by a third party but attributing portions to the user. 5. The method of claim 1, wherein: the first dataset includes at least one piece of personalized information for a first social networking connection of the first user, where the first user is connected to the first social networking connection on a first social networking service. 6. The method of claim 5, wherein: the first dataset further includes at least one piece of personalized information for a second social networking connection, where the second social networking connection is connected to the first social networking connection on the first social networking service. 7. A computer program product comprising a computer readable storage medium having stored thereon: program instructions to receive a first dataset pertaining to a first user; program instructions to build, utilizing the first dataset, an ontology of artifacts known to the first user, where the ontology includes a domain of food and a plurality of artifacts that include food recipes, and where the artifacts have corresponding characteristics that include food ingredients; program instructions to calculate a prior probability distribution for each artifact of the ontology using a probabilistic familiarity algorithm, wherein calculating the prior probability distribution for a given artifact using the probabilistic familiarity algorithm includes: determining an ontology closeness representing an amount of closeness between the given artifact and a set of artifacts that are determined to be similar to the given artifact based on the respective characteristics of the artifacts, determining a social proximity measure representing an amount of closeness between the first user and a set of persons within a social network of the first user, determining a temporal proximity measure representing an amount of closeness between a time the given artifact was observed by the first user and a set of time points prior to the time the given artifact was observed by the first user, and utilizing the ontology closeness, the social proximity measure, and the temporal proximity measure to create a weighted frequency for the given artifact, the weighted frequency corresponding to the prior probability distribution; program instructions to receive the identity of a first artifact, wherein the first artifact is not included in the ontology; and program instructions to calculate a probabilistic familiarity value for the first artifact with respect to the first user by adding the first artifact to the set of artifacts and calculating the first artifact's prior probability distribution using the probabilistic familiarity algorithm. 8. The computer program product of claim 7, further comprising: program instructions to determine if the probabilistic familiarity value is below a first probabilistic familiarity threshold, wherein if the probabilistic familiarity value is below the first probabilistic familiarity threshold, the first artifact is added to a set of artifact(s) that are not familiar to the first user. 9. The computer program product of claim 7, wherein: the probabilistic familiarity algorithm is a Bayesian surprise-based algorithm. 10. The computer program product of claim 7, wherein: the first dataset includes at least one piece of personalized information for the first user; and the at least one piece of personalized information is selected from the group consisting of: activity by the user on a social networking service (SNS); activity viewable by the user on a SNS; online purchase history of the user; online browser history of the user; website content created by the user; and content created by a third party but attributing portions to the user. 11. The computer program product of claim 7, wherein: the first dataset includes at least one piece of personalized information for a first social networking connection of the first user, where the first user is connected to the first social networking connection on a first social networking service. 12. The computer program product of claim 11, wherein: the first dataset further includes at least one piece of personalized information for a second social networking connection, where the second social networking connection is connected to the first social networking connection on the first social networking service. 13. A computer system comprising: a processor(s) set; and a computer readable storage medium; wherein: the processor set is structured, located, connected and/or programmed to run program instructions stored on the computer readable storage medium; and the program instructions include: program instructions to receive a first dataset pertaining to a first user; program instructions to build, utilizing the first dataset, an ontology of artifacts known to the first user, where the ontology includes a domain of food and a plurality of artifacts that include food recipes, and where the artifacts have corresponding characteristics that include food ingredients; program instructions to calculate a prior probability distribution for each artifact of the ontology using a probabilistic familiarity algorithm, wherein calculating the prior probability distribution for a given artifact using the probabilistic familiarity algorithm includes: determining an ontology closeness representing an amount of closeness between the given artifact and a set of artifacts that are determined to be similar to the given artifact based on the respective characteristics of the artifacts, determining a social proximity measure representing an amount of closeness between the first user and a set of persons within a social network of the first user, determining a temporal proximity measure representing an amount of closeness between a time the given artifact was observed by the first user and a set of time points prior to the time the given artifact was observed by the first user, and utilizing the ontology closeness, the social proximity measure, and the temporal proximity measure to create a weighted frequency for the given artifact, the weighted frequency corresponding to the prior probability distribution; program instructions to receive the identity of a first artifact, wherein the first artifact is not included in the ontology; and program instructions to calculate a probabilistic familiarity value for the first artifact with respect to the first user by adding the first artifact to the set of artifacts and calculating the first artifact's prior probability distribution using the probabilistic familiarity algorithm. 14. The computer system of claim 13, wherein the program instructions further comprise: program instructions to determine if the probabilistic familiarity value is below a first probabilistic familiarity threshold, wherein if the probabilistic familiarity value is below the first probabilistic familiarity threshold, the first artifact is added to a set of artifact(s) that are not familiar to the first user. 15. The computer system of claim 13, wherein: the probabilistic familiarity algorithm is a Bayesian surprise-based algorithm. 16. The computer system of claim 13, wherein: the first dataset includes at least one piece of personalized information for the first user; and the at least one piece of personalized information is selected from the group consisting of: activity by the user on a social networking service (SNS); activity viewable by the user on a SNS; online purchase history of the user; online browser history of the user; website content created by the user; and content created by a third party but attributing portions to the user. 17. The computer system of claim 13, wherein: the first dataset includes at least one piece of personalized information for a first social networking connection of the first user, where the first user is connected to the first social networking connection on a first social networking service. 18. The computer system of claim 17, wherein: the first dataset further includes at least one piece of personalized information for a second social networking connection, where the second social networking connection is connected to the first social networking connection on the first social networking service.